{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别与计算机视觉文档实践\n",
    "\n",
    ">* 本周主要内容：人脸（Face） API文档不同平台对比与实践及计算机视觉入门（认知服务）\n",
    ">* 202009_API_人工智能与机器学习_week03\n",
    ">*  电子讲义设计者：许智超\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "## 复习\n",
    "\n",
    "* 上周主要内容： \n",
    ">    * 1、API文档\n",
    ">    * 2、认知服务-人脸识别\n",
    ">    * 3、pandas黑魔法（json_normalize）\n",
    "-----\n",
    "* 提问、检查与扩展：\n",
    ">    * 1、上周的Azure face API随机检查2位同学，对于API文档是否有能力详细阅读？\n",
    ">    * 2、抽查2位同学，对于获取的数据用途有何思考？是否尝试其他平台（XXX API、XXX API）      \n",
    ">    * 3、扩展: 我们尝试抽取了4张图片，包含AB、ABC、BCD、ABCD四个人物特征，如何通过API检查数据差异？\n",
    "  \n",
    "\n",
    "-----\n",
    "## 本周学习目标：\n",
    "\n",
    "> $\\mathcal{1、Azure 认知服务-人脸集合演示}$     \n",
    "> $\\mathcal{2、Face++ 之 FaceSets 实践}$     \n",
    "> $\\mathcal{3、计算机视觉实践}$    \n",
    "\n",
    "\n",
    "\n",
    "## 学生权限\n",
    "\n",
    "* [访问学生权益](https://azure.microsoft.com/zh-cn/education/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "/* 本电子讲义使用之CSS */\n",
       "div.code_cell {\n",
       "    background-color: #e5f1fe;\n",
       "}\n",
       "div.cell.selected {\n",
       "    background-color: #effee2;\n",
       "    font-size: 2rem;\n",
       "    line-height: 2.4rem;\n",
       "}\n",
       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 50%;\n",
       "    height: auto;\n",
       "    float: center;\n",
       "}\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "\n",
       "</style>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #e5f1fe;\n",
    "}\n",
    "div.cell.selected {\n",
    "    background-color: #effee2;\n",
    "    font-size: 2rem;\n",
    "    line-height: 2.4rem;\n",
    "}\n",
    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
    "}\n",
    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 50%;\n",
    "    height: auto;\n",
    "    float: center;\n",
    "}\n",
    "/* Gradient transparent - color - transparent */\n",
    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复习\n",
    "\n",
    "> 1、回顾阅读API文档的关键点     \n",
    "> 2、正确阅读json数据 [jsonviewer.stack.hu(json转换检查数据)](http://jsonviewer.stack.hu)    \n",
    "> 3、pandas 中的json_normalize模块/函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-1 面部检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-1 面部检测\n",
    "import requests\n",
    "# json\n",
    "import json\n",
    "\n",
    "# set to your own subscription key value\n",
    "subscription_key = \"9415313ee78e4d79a0d6cf603d0177f0\"\n",
    "assert subscription_key\n",
    "\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-gzf123.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文body\n",
    "image_url = 'https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603257280786&di=7230447b35ba7ae19bd202b247c437b7&imgtype=0&src=http%3A%2F%2Fb-ssl.duitang.com%2Fuploads%2Fitem%2F201812%2F02%2F20181202090740_nshlb.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数parameters\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->bytes\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-2 json转译\n",
    "\n",
    "> * bytes ---> json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': '83c7c58e-a24c-4448-aa84-f39b0d47f2ae',\n",
       "  'faceRectangle': {'top': 160, 'left': 772, 'width': 193, 'height': 193},\n",
       "  'faceAttributes': {'smile': 0.976,\n",
       "   'headPose': {'pitch': -9.4, 'roll': -15.8, 'yaw': -15.7},\n",
       "   'gender': 'female',\n",
       "   'age': 20.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.009,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.976,\n",
       "    'neutral': 0.015,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.19},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.59},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.16},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.04,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'blond', 'confidence': 0.99},\n",
       "     {'color': 'brown', 'confidence': 0.81},\n",
       "     {'color': 'red', 'confidence': 0.45},\n",
       "     {'color': 'gray', 'confidence': 0.21},\n",
       "     {'color': 'other', 'confidence': 0.18},\n",
       "     {'color': 'black', 'confidence': 0.04},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': 'f2c1a1e6-1d18-41ae-b3c4-288b4861d947',\n",
       "  'faceRectangle': {'top': 219, 'left': 581, 'width': 149, 'height': 149},\n",
       "  'faceAttributes': {'smile': 0.998,\n",
       "   'headPose': {'pitch': -5.7, 'roll': -34.6, 'yaw': -5.8},\n",
       "   'gender': 'female',\n",
       "   'age': 23.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.001,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.998,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.16},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.52},\n",
       "   'noise': {'noiseLevel': 'high', 'value': 1.0},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.02,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 0.99},\n",
       "     {'color': 'brown', 'confidence': 0.96},\n",
       "     {'color': 'other', 'confidence': 0.31},\n",
       "     {'color': 'gray', 'confidence': 0.26},\n",
       "     {'color': 'red', 'confidence': 0.08},\n",
       "     {'color': 'blond', 'confidence': 0.02},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '04f1f066-6c10-4bc9-b760-576a5a5613f6',\n",
       "  'faceRectangle': {'top': 174, 'left': 154, 'width': 140, 'height': 140},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': -14.9, 'roll': -3.9, 'yaw': 17.8},\n",
       "   'gender': 'female',\n",
       "   'age': 21.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'medium', 'value': 0.33},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.68},\n",
       "   'noise': {'noiseLevel': 'high', 'value': 0.75},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.13,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'brown', 'confidence': 0.92},\n",
       "     {'color': 'blond', 'confidence': 0.75},\n",
       "     {'color': 'black', 'confidence': 0.43},\n",
       "     {'color': 'red', 'confidence': 0.29},\n",
       "     {'color': 'gray', 'confidence': 0.26},\n",
       "     {'color': 'other', 'confidence': 0.22},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '5fe36983-d06a-4caa-b02a-73f7722e6096',\n",
       "  'faceRectangle': {'top': 158, 'left': 350, 'width': 126, 'height': 126},\n",
       "  'faceAttributes': {'smile': 0.994,\n",
       "   'headPose': {'pitch': -18.0, 'roll': 11.0, 'yaw': 7.2},\n",
       "   'gender': 'female',\n",
       "   'age': 20.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.002,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.994,\n",
       "    'neutral': 0.004,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'medium', 'value': 0.26},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.7},\n",
       "   'noise': {'noiseLevel': 'medium', 'value': 0.55},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.56,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'brown', 'confidence': 0.95},\n",
       "     {'color': 'black', 'confidence': 0.9},\n",
       "     {'color': 'gray', 'confidence': 0.39},\n",
       "     {'color': 'blond', 'confidence': 0.27},\n",
       "     {'color': 'other', 'confidence': 0.19},\n",
       "     {'color': 'red', 'confidence': 0.09},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-2\n",
    "results = response.json()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-3 pandas 数据表格化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceRectangle.top</th>\n",
       "      <th>faceRectangle.left</th>\n",
       "      <th>faceRectangle.width</th>\n",
       "      <th>faceRectangle.height</th>\n",
       "      <th>faceAttributes.smile</th>\n",
       "      <th>faceAttributes.headPose.pitch</th>\n",
       "      <th>faceAttributes.headPose.roll</th>\n",
       "      <th>faceAttributes.headPose.yaw</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "      <th>...</th>\n",
       "      <th>faceAttributes.noise.value</th>\n",
       "      <th>faceAttributes.makeup.eyeMakeup</th>\n",
       "      <th>faceAttributes.makeup.lipMakeup</th>\n",
       "      <th>faceAttributes.accessories</th>\n",
       "      <th>faceAttributes.occlusion.foreheadOccluded</th>\n",
       "      <th>faceAttributes.occlusion.eyeOccluded</th>\n",
       "      <th>faceAttributes.occlusion.mouthOccluded</th>\n",
       "      <th>faceAttributes.hair.bald</th>\n",
       "      <th>faceAttributes.hair.invisible</th>\n",
       "      <th>faceAttributes.hair.hairColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83c7c58e-a24c-4448-aa84-f39b0d47f2ae</td>\n",
       "      <td>160</td>\n",
       "      <td>772</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>0.976</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>-15.8</td>\n",
       "      <td>-15.7</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.16</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'blond', 'confidence': 0.99}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f2c1a1e6-1d18-41ae-b3c4-288b4861d947</td>\n",
       "      <td>219</td>\n",
       "      <td>581</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>0.998</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-34.6</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 0.99}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04f1f066-6c10-4bc9-b760-576a5a5613f6</td>\n",
       "      <td>174</td>\n",
       "      <td>154</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-14.9</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.13</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'brown', 'confidence': 0.92}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5fe36983-d06a-4caa-b02a-73f7722e6096</td>\n",
       "      <td>158</td>\n",
       "      <td>350</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>0.994</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.56</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'brown', 'confidence': 0.95}, {'col...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId  faceRectangle.top  \\\n",
       "0  83c7c58e-a24c-4448-aa84-f39b0d47f2ae                160   \n",
       "1  f2c1a1e6-1d18-41ae-b3c4-288b4861d947                219   \n",
       "2  04f1f066-6c10-4bc9-b760-576a5a5613f6                174   \n",
       "3  5fe36983-d06a-4caa-b02a-73f7722e6096                158   \n",
       "\n",
       "   faceRectangle.left  faceRectangle.width  faceRectangle.height  \\\n",
       "0                 772                  193                   193   \n",
       "1                 581                  149                   149   \n",
       "2                 154                  140                   140   \n",
       "3                 350                  126                   126   \n",
       "\n",
       "   faceAttributes.smile  faceAttributes.headPose.pitch  \\\n",
       "0                 0.976                           -9.4   \n",
       "1                 0.998                           -5.7   \n",
       "2                 1.000                          -14.9   \n",
       "3                 0.994                          -18.0   \n",
       "\n",
       "   faceAttributes.headPose.roll  faceAttributes.headPose.yaw  \\\n",
       "0                         -15.8                        -15.7   \n",
       "1                         -34.6                         -5.8   \n",
       "2                          -3.9                         17.8   \n",
       "3                          11.0                          7.2   \n",
       "\n",
       "  faceAttributes.gender  ...  faceAttributes.noise.value  \\\n",
       "0                female  ...                        0.16   \n",
       "1                female  ...                        1.00   \n",
       "2                female  ...                        0.75   \n",
       "3                female  ...                        0.55   \n",
       "\n",
       "   faceAttributes.makeup.eyeMakeup  faceAttributes.makeup.lipMakeup  \\\n",
       "0                             True                             True   \n",
       "1                             True                             True   \n",
       "2                             True                             True   \n",
       "3                             True                             True   \n",
       "\n",
       "   faceAttributes.accessories faceAttributes.occlusion.foreheadOccluded  \\\n",
       "0                          []                                     False   \n",
       "1                          []                                     False   \n",
       "2                          []                                     False   \n",
       "3                          []                                     False   \n",
       "\n",
       "   faceAttributes.occlusion.eyeOccluded  \\\n",
       "0                                 False   \n",
       "1                                 False   \n",
       "2                                 False   \n",
       "3                                 False   \n",
       "\n",
       "   faceAttributes.occlusion.mouthOccluded  faceAttributes.hair.bald  \\\n",
       "0                                   False                      0.04   \n",
       "1                                   False                      0.02   \n",
       "2                                   False                      0.13   \n",
       "3                                   False                      0.56   \n",
       "\n",
       "   faceAttributes.hair.invisible  \\\n",
       "0                          False   \n",
       "1                          False   \n",
       "2                          False   \n",
       "3                          False   \n",
       "\n",
       "                       faceAttributes.hair.hairColor  \n",
       "0  [{'color': 'blond', 'confidence': 0.99}, {'col...  \n",
       "1  [{'color': 'black', 'confidence': 0.99}, {'col...  \n",
       "2  [{'color': 'brown', 'confidence': 0.92}, {'col...  \n",
       "3  [{'color': 'brown', 'confidence': 0.95}, {'col...  \n",
       "\n",
       "[4 rows x 38 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3\n",
    "import pandas as pd\n",
    "df_face = pd.json_normalize(results)\n",
    "df_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-4 数据取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['83c7c58e-a24c-4448-aa84-f39b0d47f2ae',\n",
       " 'f2c1a1e6-1d18-41ae-b3c4-288b4861d947',\n",
       " '04f1f066-6c10-4bc9-b760-576a5a5613f6',\n",
       " '5fe36983-d06a-4caa-b02a-73f7722e6096']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceID = df_face['faceId'].values.tolist()\n",
    "faceID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['faceId', 'faceRectangle.top', 'faceRectangle.left',\n",
       "       'faceRectangle.width', 'faceRectangle.height', 'faceAttributes.smile',\n",
       "       'faceAttributes.headPose.pitch', 'faceAttributes.headPose.roll',\n",
       "       'faceAttributes.headPose.yaw', 'faceAttributes.gender',\n",
       "       'faceAttributes.age', 'faceAttributes.facialHair.moustache',\n",
       "       'faceAttributes.facialHair.beard',\n",
       "       'faceAttributes.facialHair.sideburns', 'faceAttributes.glasses',\n",
       "       'faceAttributes.emotion.anger', 'faceAttributes.emotion.contempt',\n",
       "       'faceAttributes.emotion.disgust', 'faceAttributes.emotion.fear',\n",
       "       'faceAttributes.emotion.happiness', 'faceAttributes.emotion.neutral',\n",
       "       'faceAttributes.emotion.sadness', 'faceAttributes.emotion.surprise',\n",
       "       'faceAttributes.blur.blurLevel', 'faceAttributes.blur.value',\n",
       "       'faceAttributes.exposure.exposureLevel',\n",
       "       'faceAttributes.exposure.value', 'faceAttributes.noise.noiseLevel',\n",
       "       'faceAttributes.noise.value', 'faceAttributes.makeup.eyeMakeup',\n",
       "       'faceAttributes.makeup.lipMakeup', 'faceAttributes.accessories',\n",
       "       'faceAttributes.occlusion.foreheadOccluded',\n",
       "       'faceAttributes.occlusion.eyeOccluded',\n",
       "       'faceAttributes.occlusion.mouthOccluded', 'faceAttributes.hair.bald',\n",
       "       'faceAttributes.hair.invisible', 'faceAttributes.hair.hairColor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查属性/特征值\n",
    "df_face.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceAttributes.glasses</th>\n",
       "      <th>faceAttributes.emotion.neutral</th>\n",
       "      <th>faceAttributes.age</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83c7c58e-a24c-4448-aa84-f39b0d47f2ae</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.015</td>\n",
       "      <td>20.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f2c1a1e6-1d18-41ae-b3c4-288b4861d947</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04f1f066-6c10-4bc9-b760-576a5a5613f6</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5fe36983-d06a-4caa-b02a-73f7722e6096</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.004</td>\n",
       "      <td>20.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId faceAttributes.glasses  \\\n",
       "0  83c7c58e-a24c-4448-aa84-f39b0d47f2ae              NoGlasses   \n",
       "1  f2c1a1e6-1d18-41ae-b3c4-288b4861d947              NoGlasses   \n",
       "2  04f1f066-6c10-4bc9-b760-576a5a5613f6              NoGlasses   \n",
       "3  5fe36983-d06a-4caa-b02a-73f7722e6096              NoGlasses   \n",
       "\n",
       "   faceAttributes.emotion.neutral  faceAttributes.age faceAttributes.gender  \n",
       "0                           0.015                20.0                female  \n",
       "1                           0.000                23.0                female  \n",
       "2                           0.000                21.0                female  \n",
       "3                           0.004                20.0                female  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可观察其中几组数据\n",
    "df_face[['faceId','faceAttributes.glasses','faceAttributes.emotion.neutral','faceAttributes.age','faceAttributes.gender']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure 认知服务-人脸演示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试一试：人脸验证(难)\n",
    "\n",
    "人脸相似度？有没有试下？\n",
    "\n",
    ">* API人脸文档中最重要的组成部分：\n",
    " >>* Request URL？\n",
    " >>* Http Method？\n",
    " >>* 参数？\n",
    "\n",
    "----\n",
    ">* 不同的人脸对比数据\n",
    "  >>* 人脸验证关键数据？\n",
    "  >>* 根据哪些数据证明是同一个人？\n",
    "    \n",
    "----\n",
    ">* 具体步骤：\n",
    "  >>* 1、Create 请求成功200 返回空字符串\n",
    "  >>* 2、Add face 请求成功200 返回persistedFaceId\n",
    "  >>* 3、Detect 准备 被检测人 人脸的id\n",
    "  >>* 4、Find similars 返回相似置信度\n",
    "  >>* 附加：get查看facelists\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02 \n",
      " https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02 \n",
      " https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02\n"
     ]
    }
   ],
   "source": [
    "# 字符串拼接练习\n",
    "faceListId = \"zhichao02\"\n",
    "# 1\n",
    "url_01 = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/\" + faceListId # string 拼接\n",
    "# 2gz\n",
    "url_02 = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/%s\" %(faceListId)\n",
    "# 3 \n",
    "url_03 = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/{}\".format(faceListId)\n",
    "print(url_01,'\\n',url_02,'\\n',url_03)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "# 1、create  列表\n",
    "# faceListId\n",
    "import requests\n",
    "import json\n",
    "faceListId =  \"blackpink0\"\n",
    "create_facelists_url =  \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "subscription_key = \"9415313ee78e4d79a0d6cf603d0177f0\"\n",
    "assert subscription_key\n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "data = {\n",
    "    \"name\":\"blackpink\",\n",
    "    \"userData\":\"共4人，4个女生\",\n",
    "    \"recognitionModel\":\"recognition_03\"\n",
    " \n",
    "}\n",
    "\n",
    "r_create = requests.put(create_facelists_url.format(faceListId),headers=headers,json=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'blackpink0',\n",
       " 'name': 'blackpink',\n",
       " 'userData': '共4人，4个女生'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/blackpink0\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers,params=data)#学生填写\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'blackpink0',\n",
       " 'name': 'blackpink',\n",
       " 'userData': '共4人，4个女生'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add face 请求成功200 返回persistedFaceId\n",
    "> 我们通过上面的步骤建好了一个脸的列表，接下来我们要给这个列表添加脸了！把我们想要对比的脸存进列表吧\n",
    "- [添加人脸进列表api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/facelist/addfacefromurl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先加一张脸试试\n",
    "# 2、Add face\n",
    "import requests\n",
    "faceListId = \"blackpink0\"\n",
    "add_face_url = 'https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/blackpink0'\n",
    "subscription_key = \"9415313ee78e4d79a0d6cf603d0177f0\"\n",
    "assert subscription_key\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "img_url = 'https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603259019640&di=73a5bee219d76628d7a1bb2002474f4d&imgtype=0&src=http%3A%2F%2Finews.gtimg.com%2Fnewsapp_bt%2F0%2F11590985606%2F1000.jpg'\n",
    "\n",
    "params_add_face={\n",
    "    \"faceListId\":\"who_you\",\n",
    "    \"userdata\":\"金智秀（JISOO）、金智妮（JENNIE）、朴彩英（ROSÉ）、LISA\"\n",
    "\n",
    "}\n",
    "\n",
    "r = r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url}) #学生填写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [404]>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'blackpink0',\n",
       " 'name': 'blackpink',\n",
       " 'userData': '共4人，4个女生'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/blackpink0\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers,params=data)#学生填写\n",
    "r_get_facelist.json()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 扩展内容，封装成函数方便多次使用 *\n",
    "> 我们要添加多张脸，但是为了减少代码量，我们可以把代码封装成函数，避免每次都要写一大堆代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 封装成函数方便添加图片/函数——可以重复使用相同的功能\n",
    "def AddFace(img_url=str,userData=str):\n",
    "    add_face_url =\"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/blackpink0/persistedFaces\"\n",
    "    assert subscription_key\n",
    "    headers = {\n",
    "        # Request headers\n",
    "        'Content-Type': 'application/json',\n",
    "        'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "    }\n",
    "    img_url = \"https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603259019640&di=73a5bee219d76628d7a1bb2002474f4d&imgtype=0&src=http%3A%2F%2Finews.gtimg.com%2Fnewsapp_bt%2F0%2F11590985606%2F1000.jpg\"\n",
    "\n",
    "    params_add_face={\n",
    "        \"userData\":userData\n",
    "    }\n",
    "    r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})\n",
    "    return r_add_face.status_code#返回出状态码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AddFace(\"https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603279444074&di=9cecf251429bc212a74ad7639e155094&imgtype=0&src=http%3A%2F%2Fhbimg.b0.upaiyun.com%2F83f564e864ce58a7c8fab21ae070fbb5705fd9ce57e76-kAuiwb_fw658\",\"LISA\")\n",
    "AddFace(\"https://ss0.bdstatic.com/70cFvHSh_Q1YnxGkpoWK1HF6hhy/it/u=2694468616,773630720&fm=26&gp=0.jpg\",\"ROSE\")\n",
    "AddFace(\"https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603258616590&di=974a8da73c68d31a10b98a9946258479&imgtype=0&src=http%3A%2F%2Fc-ssl.duitang.com%2Fuploads%2Fitem%2F202002%2F01%2F20200201134304_veh38.jpeg\",\"JENNIE\")\n",
    "AddFace(\"https://ss1.bdstatic.com/70cFvXSh_Q1YnxGkpoWK1HF6hhy/it/u=1905921636,416698009&fm=26&gp=0.jpg\",\"JISOO\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'blackpink0',\n",
       " 'name': 'blackpink',\n",
       " 'userData': '共4人，4个女生'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/blackpink0\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)#学生填写\n",
    "r_get_facelist.json()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'blackpink0' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-dc552f2fdc69>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 键/值\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mfaceListId\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[1;34m\"blackpink0\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mblackpink0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;31m#     print(i)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"userData\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"ROSE\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'blackpink0' is not defined"
     ]
    }
   ],
   "source": [
    "# 键/值\n",
    "faceListId =  \"blackpink0\"\n",
    "for i in blackpink0:\n",
    "#     print(i)\n",
    "    if i[\"userData\"] == \"ROSE\":\n",
    "        faceId_02 = i['persistedFaceId']\n",
    "faceId_02\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'r_get_facelist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-519711d0dfa7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfaceId\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mr_get_facelist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'persistedFaces'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mfaceId\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'r_get_facelist' is not defined"
     ]
    }
   ],
   "source": [
    "faceId =  r_get_facelist.json()['persistedFaces']\n",
    "faceId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'blackpink01' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-07f3a2df9325>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mblackpink01\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#     print(item)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'userData'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'JISOO'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'persistedFaceId'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mdelate_face\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'persistedFaceId'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'blackpink01' is not defined"
     ]
    }
   ],
   "source": [
    "for item in blackpink01:\n",
    "#     print(item)\n",
    "    if item['userData'] == 'JISOO':\n",
    "        print(item['persistedFaceId'])\n",
    "        delate_face = item['persistedFaceId']\n",
    "delate_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delate face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect face 删除列表内人脸id\n",
    "faceListId = \"blackpink01\"\n",
    "delete_face_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/blcakpink0\"\n",
    "\n",
    "assert subscription_key\n",
    "# 例如：删除黄志毅： {'persistedFaceId': '69103b48-b6c4-4f58-8ac1-4c8b84e56bc1','userData': '黄智毅'},\n",
    "\n",
    "persistedFaceId =  r_add_face.json()\n",
    "# 直接取上面获得的ID{'persistedFaceId': 'f18450d3-60d2-45f3-a69e-783574dc3ce8'} \n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "\n",
    "# 注意requests请求为delete\n",
    "r_delete_face = requests.delete(delete_face_url.format(faceListId,persistedFaceId),headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'blackpink01',\n",
       " 'name': 'blackpink',\n",
       " 'userData': '共4人，4个女生'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/blackpink01\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers,)#学生填写\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find similars 返回相似置信度\n",
    "- [监测人脸相似度api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/face/findsimilar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': '4beb073f-7fdf-4d37-9d61-d83c07c396d6',\n",
       "  'faceRectangle': {'top': 267, 'left': 118, 'width': 450, 'height': 450},\n",
       "  'faceAttributes': {'smile': 0.002,\n",
       "   'headPose': {'pitch': -16.5, 'roll': 4.8, 'yaw': -0.5},\n",
       "   'gender': 'female',\n",
       "   'age': 20.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.002,\n",
       "    'neutral': 0.995,\n",
       "    'sadness': 0.002,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'medium', 'value': 0.72},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.47},\n",
       "   'noise': {'noiseLevel': 'medium', 'value': 0.65},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.04,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'brown', 'confidence': 1.0},\n",
       "     {'color': 'red', 'confidence': 0.71},\n",
       "     {'color': 'black', 'confidence': 0.68},\n",
       "     {'color': 'blond', 'confidence': 0.11},\n",
       "     {'color': 'other', 'confidence': 0.1},\n",
       "     {'color': 'gray', 'confidence': 0.03},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect 检测人脸的id\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-gzf123.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文\n",
    "image_url = 'https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603281964142&di=0643d065964179f24e53677b5fb53f7c&imgtype=0&src=http%3A%2F%2Fpic3.zhimg.com%2F50%2Fv2-77acb6241ff2e55930ef74e17d977903_hd.jpg'\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 选择model\n",
    "    'recognitionModel':'recognition_03',#此参数需与facelist参数一致\n",
    "    'detectionModel':'detection_01',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes':'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->字符串\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect 检测人脸的id\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-gzf123.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文\n",
    "image_url = 'https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603281964142&di=0643d065964179f24e53677b5fb53f7c&imgtype=0&src=http%3A%2F%2Fpic3.zhimg.com%2F50%2Fv2-77acb6241ff2e55930ef74e17d977903_hd.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 选择model\n",
    "    'recognitionModel':'recognition_03',#此参数需与facelist参数一致\n",
    "    'detectionModel':'detection_01',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "findsimilars_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/findsimilars\"\n",
    "\n",
    "# 请求正文 faceId需要先检测一张照片获取\n",
    "data_findsimilars = {\n",
    "    \"faceId\":\"4beb073f-7fdf-4d37-9d61-d83c07c396d6\",#取上方的faceID\n",
    "    \"faceListId\": \"blackpink0\",\n",
    "    \"maxNumOfCandidatesReturned\": 10,\n",
    "    \"mode\": \"matchFace\"#matchPerson #一种为验证模式，一种为相似值模式\n",
    "    }\n",
    "\n",
    "r_findsimilars = requests.post(findsimilars_url,headers=headers,json=data_findsimilars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': '4278a690-5f78-40d7-abe5-88eab5d5494f',\n",
       "  'confidence': 0.29269},\n",
       " {'persistedFaceId': '8b1c2538-05cc-4636-8460-cfa8059d5c72',\n",
       "  'confidence': 0.20908},\n",
       " {'persistedFaceId': '5bfad450-0274-40b5-ba9f-0e4a0af9763b',\n",
       "  'confidence': 0.17849},\n",
       " {'persistedFaceId': 'c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1',\n",
       "  'confidence': 0.16209},\n",
       " {'persistedFaceId': 'e61d920f-f131-464a-9a43-a82babf20358',\n",
       "  'confidence': 0.15023},\n",
       " {'persistedFaceId': '6a663a8e-725c-4c7d-bd72-5520c4a2e93e',\n",
       "  'confidence': 0.101},\n",
       " {'persistedFaceId': '8a559e64-a44b-4a4b-84dc-e14da959da3a',\n",
       "  'confidence': 0.10034},\n",
       " {'persistedFaceId': '0523267b-6750-4d1e-987d-674e0ecb0821',\n",
       "  'confidence': 0.0999},\n",
       " {'persistedFaceId': '5081698a-efe4-4be9-822c-cb58f2cc4803',\n",
       "  'confidence': 0.09955},\n",
       " {'persistedFaceId': 'd0c25191-8224-410e-9eed-df3457e0a77f',\n",
       "  'confidence': 0.09503}]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5081698a-efe4-4be9-822c-cb58f2cc4803</td>\n",
       "      <td>丘某峰</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8cfa1b6d-b087-4cd2-be8c-55582168b497</td>\n",
       "      <td>丘天惠</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8a559e64-a44b-4a4b-84dc-e14da959da3a</td>\n",
       "      <td>林嘉茵</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7f5db725-735d-4b4f-a91b-42ac215b5aa9</td>\n",
       "      <td>汤玲萍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e386e027-8299-4703-a748-8ab7d060348c</td>\n",
       "      <td>曾雯燕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6d3cb16e-239d-495a-a3cc-b6f346b07034</td>\n",
       "      <td>谢依希</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>81ec76dd-163f-4ee3-aa16-11a0ce73295e</td>\n",
       "      <td>杨悦聪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6c940c74-645a-45c8-884d-6ab34ba352c2</td>\n",
       "      <td>周雨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d8c64294-0629-4e81-87d5-3976ad54ac0e</td>\n",
       "      <td>刘瑜鹏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1</td>\n",
       "      <td>陈嘉仪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4278a690-5f78-40d7-abe5-88eab5d5494f</td>\n",
       "      <td>徐旖芊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>e61d920f-f131-464a-9a43-a82babf20358</td>\n",
       "      <td>刘心如</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>a968313c-ab4e-42d0-9265-749dcf9b8529</td>\n",
       "      <td>刘宇</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8b1c2538-05cc-4636-8460-cfa8059d5c72</td>\n",
       "      <td>李婷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>d0c25191-8224-410e-9eed-df3457e0a77f</td>\n",
       "      <td>黄智毅</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>454e8382-9ee5-4264-b97f-02ecbe5bd987</td>\n",
       "      <td>黄慧文</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5bfad450-0274-40b5-ba9f-0e4a0af9763b</td>\n",
       "      <td>张铭睿</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>a9c9451c-8627-4eed-b587-9410d5a61ff9</td>\n",
       "      <td>陈婷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6a663a8e-725c-4c7d-bd72-5520c4a2e93e</td>\n",
       "      <td>洪可凡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>050bf519-7cdf-4b6a-8aac-52f475d282f8</td>\n",
       "      <td>卢继志</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0523267b-6750-4d1e-987d-674e0ecb0821</td>\n",
       "      <td>张梓乐</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         persistedFaceId userData\n",
       "0   5081698a-efe4-4be9-822c-cb58f2cc4803      丘某峰\n",
       "1   8cfa1b6d-b087-4cd2-be8c-55582168b497      丘天惠\n",
       "2   8a559e64-a44b-4a4b-84dc-e14da959da3a      林嘉茵\n",
       "3   7f5db725-735d-4b4f-a91b-42ac215b5aa9      汤玲萍\n",
       "4   e386e027-8299-4703-a748-8ab7d060348c      曾雯燕\n",
       "5   6d3cb16e-239d-495a-a3cc-b6f346b07034      谢依希\n",
       "6   81ec76dd-163f-4ee3-aa16-11a0ce73295e      杨悦聪\n",
       "7   6c940c74-645a-45c8-884d-6ab34ba352c2       周雨\n",
       "8   d8c64294-0629-4e81-87d5-3976ad54ac0e      刘瑜鹏\n",
       "9   c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1      陈嘉仪\n",
       "10  4278a690-5f78-40d7-abe5-88eab5d5494f      徐旖芊\n",
       "11  e61d920f-f131-464a-9a43-a82babf20358      刘心如\n",
       "12  a968313c-ab4e-42d0-9265-749dcf9b8529       刘宇\n",
       "13  8b1c2538-05cc-4636-8460-cfa8059d5c72       李婷\n",
       "14  d0c25191-8224-410e-9eed-df3457e0a77f      黄智毅\n",
       "15  454e8382-9ee5-4264-b97f-02ecbe5bd987      黄慧文\n",
       "16  5bfad450-0274-40b5-ba9f-0e4a0af9763b      张铭睿\n",
       "17  a9c9451c-8627-4eed-b587-9410d5a61ff9       陈婷\n",
       "18  6a663a8e-725c-4c7d-bd72-5520c4a2e93e      洪可凡\n",
       "19  050bf519-7cdf-4b6a-8aac-52f475d282f8      卢继志\n",
       "20  0523267b-6750-4d1e-987d-674e0ecb0821      张梓乐"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#facelist里面的数据\n",
    "faceListId_df = pd.json_normalize(r_get_facelist.json()[\"persistedFaces\"])# 升级pandas才能运行\n",
    "faceListId_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4278a690-5f78-40d7-abe5-88eab5d5494f</td>\n",
       "      <td>0.29269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8b1c2538-05cc-4636-8460-cfa8059d5c72</td>\n",
       "      <td>0.20908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5bfad450-0274-40b5-ba9f-0e4a0af9763b</td>\n",
       "      <td>0.17849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1</td>\n",
       "      <td>0.16209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e61d920f-f131-464a-9a43-a82babf20358</td>\n",
       "      <td>0.15023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6a663a8e-725c-4c7d-bd72-5520c4a2e93e</td>\n",
       "      <td>0.10100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8a559e64-a44b-4a4b-84dc-e14da959da3a</td>\n",
       "      <td>0.10034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0523267b-6750-4d1e-987d-674e0ecb0821</td>\n",
       "      <td>0.09990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5081698a-efe4-4be9-822c-cb58f2cc4803</td>\n",
       "      <td>0.09955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>d0c25191-8224-410e-9eed-df3457e0a77f</td>\n",
       "      <td>0.09503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId  confidence\n",
       "0  4278a690-5f78-40d7-abe5-88eab5d5494f     0.29269\n",
       "1  8b1c2538-05cc-4636-8460-cfa8059d5c72     0.20908\n",
       "2  5bfad450-0274-40b5-ba9f-0e4a0af9763b     0.17849\n",
       "3  c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1     0.16209\n",
       "4  e61d920f-f131-464a-9a43-a82babf20358     0.15023\n",
       "5  6a663a8e-725c-4c7d-bd72-5520c4a2e93e     0.10100\n",
       "6  8a559e64-a44b-4a4b-84dc-e14da959da3a     0.10034\n",
       "7  0523267b-6750-4d1e-987d-674e0ecb0821     0.09990\n",
       "8  5081698a-efe4-4be9-822c-cb58f2cc4803     0.09955\n",
       "9  d0c25191-8224-410e-9eed-df3457e0a77f     0.09503"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回相似度的数据\n",
    "find_df = pd.json_normalize(r_findsimilars.json())# 升级pandas才能运行\n",
    "find_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4278a690-5f78-40d7-abe5-88eab5d5494f</td>\n",
       "      <td>徐旖芊</td>\n",
       "      <td>0.29269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8b1c2538-05cc-4636-8460-cfa8059d5c72</td>\n",
       "      <td>李婷</td>\n",
       "      <td>0.20908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5bfad450-0274-40b5-ba9f-0e4a0af9763b</td>\n",
       "      <td>张铭睿</td>\n",
       "      <td>0.17849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1</td>\n",
       "      <td>陈嘉仪</td>\n",
       "      <td>0.16209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e61d920f-f131-464a-9a43-a82babf20358</td>\n",
       "      <td>刘心如</td>\n",
       "      <td>0.15023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6a663a8e-725c-4c7d-bd72-5520c4a2e93e</td>\n",
       "      <td>洪可凡</td>\n",
       "      <td>0.10100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8a559e64-a44b-4a4b-84dc-e14da959da3a</td>\n",
       "      <td>林嘉茵</td>\n",
       "      <td>0.10034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0523267b-6750-4d1e-987d-674e0ecb0821</td>\n",
       "      <td>张梓乐</td>\n",
       "      <td>0.09990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5081698a-efe4-4be9-822c-cb58f2cc4803</td>\n",
       "      <td>丘某峰</td>\n",
       "      <td>0.09955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d0c25191-8224-410e-9eed-df3457e0a77f</td>\n",
       "      <td>黄智毅</td>\n",
       "      <td>0.09503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId userData  confidence\n",
       "3  4278a690-5f78-40d7-abe5-88eab5d5494f      徐旖芊     0.29269\n",
       "5  8b1c2538-05cc-4636-8460-cfa8059d5c72       李婷     0.20908\n",
       "7  5bfad450-0274-40b5-ba9f-0e4a0af9763b      张铭睿     0.17849\n",
       "2  c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1      陈嘉仪     0.16209\n",
       "4  e61d920f-f131-464a-9a43-a82babf20358      刘心如     0.15023\n",
       "8  6a663a8e-725c-4c7d-bd72-5520c4a2e93e      洪可凡     0.10100\n",
       "1  8a559e64-a44b-4a4b-84dc-e14da959da3a      林嘉茵     0.10034\n",
       "9  0523267b-6750-4d1e-987d-674e0ecb0821      张梓乐     0.09990\n",
       "0  5081698a-efe4-4be9-822c-cb58f2cc4803      丘某峰     0.09955\n",
       "6  d0c25191-8224-410e-9eed-df3457e0a77f      黄智毅     0.09503"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(faceListId_df, find_df,how='inner', on='persistedFaceId').sort_values(by=\"confidence\",ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设计人脸识别门禁/打卡/签到 小程序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face++ FaceSets 实践\n",
    "\n",
    "\n",
    ">* 1. FaceSet Create\n",
    ">* 2. FaceSet GetDetail\n",
    ">* 3. FaceSet AddFace\n",
    ">* 4. FaceSet RemoveFace\n",
    ">* 5. FaceSet Update\n",
    ">* 6. Compare Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_secret = \"wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG\"\n",
    "api_key = \"AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd\"  # Replace with a valid Subscription Key here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Create（创建人脸集合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. FaceSet Create\n",
    "import requests,json\n",
    "\n",
    "display_name = \"blackpink合集2\"\n",
    "outer_id = \"bp2\"\n",
    "user_data = \"4人，4个女生\"\n",
    "\n",
    "CreateFace_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/create\"\n",
    "payload = {\n",
    "    'api_key': 'AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd',\n",
    "    'api_secret': 'wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG',\n",
    "    'display_name':display_name,\n",
    "    'outer_id':outer_id,\n",
    "    'user_data':user_data\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(CreateFace_Url, params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'a1b72a5c07e97db3316fc5c1bb0044f7',\n",
       " 'time_used': 169,\n",
       " 'face_count': 0,\n",
       " 'face_added': 0,\n",
       " 'request_id': '1603291637,59410e98-7433-4991-aeff-37f5e1a81fe3',\n",
       " 'outer_id': 'bp2',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet GetDetail（获取人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "GetDetail_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/getdetail\"\n",
    "\n",
    "payload = {\n",
    "  'api_key': 'AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd',\n",
    "    'api_secret': 'wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG',\n",
    "    'outer_id':outer_id,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(GetDetail_Url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'a1b72a5c07e97db3316fc5c1bb0044f7',\n",
       " 'tags': '',\n",
       " 'time_used': 85,\n",
       " 'user_data': '4人，4个女生',\n",
       " 'display_name': 'blackpink合集2',\n",
       " 'face_tokens': [],\n",
       " 'face_count': 0,\n",
       " 'request_id': '1603291682,d4717677-c3ba-4b41-a493-7d0e59e81e45',\n",
       " 'outer_id': 'bp2'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet AddFace（增加人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "AddFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/addface\"\n",
    "\n",
    "payload = {\n",
    "   'api_key': 'AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd',\n",
    "    'api_secret': 'wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG',\n",
    "    'faceset_token':'a1b72a5c07e97db3316fc5c1bb0044f7',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(AddFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'a1b72a5c07e97db3316fc5c1bb0044f7',\n",
       " 'time_used': 96,\n",
       " 'face_count': 0,\n",
       " 'face_added': 0,\n",
       " 'request_id': '1603295025,18e0da47-ec6f-4ecf-a055-ce20509eac97',\n",
       " 'outer_id': 'bp2',\n",
       " 'failure_detail': [{'reason': 'INVALID_FACE_TOKEN',\n",
       "   'face_token': 'b0407b9e803ebd39d511cd7956fd5bf5'}]}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet RemoveFace（移除人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "RemoveFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/removeface\"\n",
    "\n",
    "payload = {\n",
    "   'api_key': 'AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd',\n",
    "    'api_secret': 'wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG',\n",
    "    'faceset_token':'a1b72a5c07e97db3316fc5c1bb0044f7',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(RemoveFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '37071d95016c1b2d81591a6f0c1681f2',\n",
       " 'face_removed': 1,\n",
       " 'time_used': 175,\n",
       " 'face_count': 0,\n",
       " 'request_id': '1602155720,7a6b9a32-08ca-4c14-981d-d4ffbd82da48',\n",
       " 'outer_id': '00001',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Update（更新人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Update_url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/update\"\n",
    "\n",
    "payload = {\n",
    "   'api_key': 'AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd',\n",
    "    'api_secret': 'wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG',\n",
    "    'faceset_token':'a1b72a5c07e97db3316fc5c1bb0044f7',\n",
    "    'user_data':\"4人，4女生\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Update_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'a1b72a5c07e97db3316fc5c1bb0044f7',\n",
       " 'request_id': '1603295125,6143f1f4-407a-4514-a79a-5b2d4ae190ff',\n",
       " 'time_used': 69,\n",
       " 'outer_id': 'bp2'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Face（对比人脸相似度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xin = \"https://wx2.sinaimg.cn/mw690/5968c483ly1gjzep8b66uj20hs0drwfk.jpg\"\n",
    "liudehua02 = \"https://tse3-mm.cn.bing.net/th/id/OIP.Xz3HbYZeNrdUnGJ7vXNzsQHaKO?pid=Api&rs=1\"\n",
    "wangzulan = \"https://tse3-mm.cn.bing.net/th/id/OIP.ZnXeGoVYT4jQudiPOGZn3QAAAA?pid=Api&rs=1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案1:直接对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "Compare_url = \"https://api-cn.faceplusplus.com/facepp/v3/compare\"\n",
    "\n",
    "payload ={\n",
    "   'api_key': 'AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd',\n",
    "    'api_secret': 'wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG',\n",
    "    'image_url1':\"https://wx2.sinaimg.cn/mw690/5968c483ly1gjzep8b66uj20hs0drwfk.jpg\",\n",
    "    'image_url2':\"https://tse3-mm.cn.bing.net/th/id/OIP.ZnXeGoVYT4jQudiPOGZn3QAAAA?pid=Api&rs=1\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Compare_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faces1': [{'face_rectangle': {'width': 125,\n",
       "    'top': 120,\n",
       "    'left': 211,\n",
       "    'height': 125},\n",
       "   'face_token': '0d13aca10479d1a3d6e5f416fdabec3a'}],\n",
       " 'faces2': [{'face_rectangle': {'width': 86,\n",
       "    'top': 91,\n",
       "    'left': 65,\n",
       "    'height': 86},\n",
       "   'face_token': '950d857df6968e2162fb6c1c48599b9d'}],\n",
       " 'time_used': 2439,\n",
       " 'thresholds': {'1e-3': 62.327, '1e-5': 73.975, '1e-4': 69.101},\n",
       " 'confidence': 18.503,\n",
       " 'image_id2': 'g6kg8zfyOouG6ftP+GvEfg==',\n",
       " 'image_id1': 'lZfYCnnS2JgC4JYOgK5Tqw==',\n",
       " 'request_id': '1603464488,1a77db2e-3add-45e7-ae14-f587215e3824'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案2:与人脸集合进行对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 面部检测(获取face_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "Detect_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect' \n",
    "xin = \"https://wx2.sinaimg.cn/mw690/5968c483ly1gjzep8b66uj20hs0drwfk.jpg\"\n",
    "img_url = xin\n",
    "\n",
    "payload = {\n",
    "    \"image_url\":\"https://gss0.baidu.com/9fo3dSag_xI4khGko9WTAnF6hhy/zhidao/pic/item/7c1ed21b0ef41bd57f7f20ff57da81cb39db3d89.jpg\",\n",
    "   'api_key': 'AAsLeQKCJe1Up-dA_ggzZGBXZq3f3brd',\n",
    "    'api_secret': 'wIm6i_GWAolQ_ArUGx9fqYE87YVE9MgG',\n",
    "    'return_attributes':'gender,age,smiling,emotion', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Detect_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1603464455,9d5b8b84-6e3f-4266-aa59-4b9ae23d41e2',\n",
       " 'time_used': 4093,\n",
       " 'faces': [{'face_token': 'a9263f8b37de34cb72b10df389325600',\n",
       "   'face_rectangle': {'top': 871, 'left': 1114, 'width': 824, 'height': 824},\n",
       "   'attributes': {'gender': {'value': 'Male'},\n",
       "    'age': {'value': 59},\n",
       "    'smile': {'value': 99.998, 'threshold': 50.0},\n",
       "    'emotion': {'anger': 0.0,\n",
       "     'disgust': 0.047,\n",
       "     'fear': 0.0,\n",
       "     'happiness': 99.945,\n",
       "     'neutral': 0.0,\n",
       "     'sadness': 0.007,\n",
       "     'surprise': 0.0}}}],\n",
       " 'image_id': 'KIOXEC2V/MyL4zuopAcNig==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加入人脸集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别社会/政治使用思考\n",
    "> 人脸识别的 API比较\n",
    ">> * [Face Off: Confronting Bias in Face Recognition AI](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    ">> * [为什么世界现在需要道德的面部识别](https://www.kairos.com/blog/why-the-world-needs-ethical-facial-recognition-now)\n",
    "    * 到2023年，全球人脸识别市场的价值估计接近100亿美元，复合年增长率为16.8％。市场背后的主要增长动力是监视市场的发展以及生物识别技术领域的政府支出。但是，面部识别的使用在其他领域也有很大贡献，例如帮助企业打击消费者欺诈，满足动态法规遵从性以及提供可获利的客户体验。\n",
    "\n",
    "-----\n",
    "> 人脸识别的偏差及API 政治经济学 \n",
    ">> * [对抗：面对面部识别AI中的偏见](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    "    * 发生了什么？\n",
    "        * “编码注视”或算法偏差的研究: 浅肤色男性的错误率为0.8％ ?  深色皮肤女性的错误率高达34.7％  ? (Microsoft，IBM和Face ++)\n",
    "    * 用户的反应范围从惊奇和赞美到不悦和冒犯\n",
    "        * 我们承认目前提供的种族分类（黑人，白人，亚裔，西班牙裔，其他则不足以代表丰富多样，发展迅速的文化和种族挂毯\n",
    "    * 该怎么办？\n",
    "        * 改善数据\n",
    "        * 寻求持续的反馈    \n",
    "        \n",
    ">>  * [科技行业没有应对面部识别偏见的计划](https://www.theverge.com/2018/7/26/17616290/facial-recognition-ai-bias-benchmark-test) \n",
    ">>> 公司在做什么？(一些高科技大公司支持基准和法规)     \n",
    ">>> 我们如何解决偏见问题？    \n",
    ">>> 偏差不是唯一的问题\n",
    "\n",
    ">> * [面部识别尚未准备好给执法部门使用](https://techcrunch.com/2018/06/25/facial-recognition-software-is-not-ready-for-use-by-law-enforcement/)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算机视觉\n",
    "\n",
    "* 学习并完成所有Azure computer version 的API调用\n",
    "> * 分析远程图像    \n",
    "> * 分析本地图片    \n",
    "> * 生成缩略图    \n",
    "> * 提取印刷体文本和手写文本    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析远程图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"categories\": [{\"name\": \"people_portrait\", \"score\": 0.56640625, \"detail\": {\"celebrities\": [{\"name\": \"Song-Kyoung Lee\", \"confidence\": 0.9989079236984253, \"faceRectangle\": {\"left\": 170, \"top\": 203, \"width\": 208, \"height\": 208}}]}}], \"color\": {\"dominantColorForeground\": \"Brown\", \"dominantColorBackground\": \"White\", \"dominantColors\": [\"White\", \"Brown\"], \"accentColor\": \"A9223D\", \"isBwImg\": false, \"isBWImg\": false}, \"description\": {\"tags\": [\"person\", \"woman\", \"clothing\", \"indoor\", \"girl\", \"beautiful\", \"hair\", \"posing\", \"young\", \"smiling\", \"holding\", \"wearing\", \"food\", \"close\", \"dress\", \"little\", \"phone\", \"eating\", \"standing\", \"plate\", \"shirt\", \"stuffed\", \"bear\"], \"captions\": [{\"text\": \"a close up of Song-Kyoung Lee\", \"confidence\": 0.9628735499041425}]}, \"requestId\": \"30c89ac4-a38a-4569-b146-4c6259fb2068\", \"metadata\": {\"height\": 560, \"width\": 430, \"format\": \"Jpeg\"}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "endpoint = \"https://computervision-gzf.cognitiveservices.azure.com/\"\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "subscription_key = \"15f98ea70f74425382a7f6b768cd9915\"\n",
    "\n",
    "# base url\n",
    "analyze_url = endpoint+ \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://n.sinaimg.cn/fashion/transform/20170120/uK5B-fxzusxt7718219.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "# 参数\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "# 请求主体body\n",
    "data = {'url': image_url}\n",
    "response = requests.post(analyze_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(json.dumps(response.json()))\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析本地图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'categories': [{'name': 'people_portrait', 'score': 0.49609375}], 'color': {'dominantColorForeground': 'White', 'dominantColorBackground': 'White', 'dominantColors': ['White'], 'accentColor': '945437', 'isBwImg': False, 'isBWImg': False}, 'description': {'tags': ['person', 'woman', 'wearing', 'hair', 'young', 'posing', 'smiling', 'holding', 'dress', 'photo', 'shirt', 'close', 'girl', 'blue', 'head', 'phone', 'eyes', 'dressed', 'neck', 'standing'], 'captions': [{'text': 'a close up of a woman', 'confidence': 0.9599742870326996}]}, 'requestId': '3a014fdc-a694-4d22-a1ef-e91bda5406fc', 'metadata': {'height': 1200, 'width': 1920, 'format': 'Jpeg'}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "endpoint = \"https://computervision-gzf.cognitiveservices.azure.com/\"\n",
    "analyze_url = endpoint + \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_path to the local path of an image that you want to analyze.\n",
    "image_path = \"D:\\qbhn.jpg\"\n",
    "# Read the image into a byte array\n",
    "image_data = open(image_path, \"rb\").read()\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"15f98ea70f74425382a7f6b768cd9915\",\n",
    "           'Content-Type': 'application/octet-stream'}\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "response = requests.post(\n",
    "    analyze_url, headers=headers, params=params, data=image_data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(analysis)\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(image_data))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成缩略图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thumbnail is 100-by-100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "thumbnail_url = \"https://computervision-gzf.cognitiveservices.azure.com/\" + \"vision/v2.1/generateThumbnail\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://wx1.sinaimg.cn/mw690/007bZFh6gy1gbkq4or2oqj31i614mgyl.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"dd748cf10bf9404399e5416d9399e218\"}\n",
    "params = {'width': '100', 'height': '100', 'smartCropping': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(thumbnail_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "thumbnail = Image.open(BytesIO(response.content))\n",
    "\n",
    "# Display the thumbnail.\n",
    "plt.imshow(thumbnail)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "# Verify the thumbnail size.\n",
    "print(\"Thumbnail is {0}-by-{1}\".format(*thumbnail.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取印刷体文本和手写文本 \n",
    "* 假如我们抓取了1000张网页，出了文本信息我们分析以外，还有每个页面的图片的信息，我们可以用提取图片文本的方式，将图片的信息也抓取下来\n",
    "* 我们进抓取了图片，想知道这些图片的内容是什么，也可以用提取文本的方式进行提取\n",
    "* ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"status\": \"running\",\n",
      "    \"createdDateTime\": \"2020-10-23T14:39:17Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-23T14:39:17Z\"\n",
      "}\n",
      "{\n",
      "    \"status\": \"succeeded\",\n",
      "    \"createdDateTime\": \"2020-10-23T14:39:17Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-23T14:39:18Z\",\n",
      "    \"analyzeResult\": {\n",
      "        \"version\": \"3.0.0\",\n",
      "        \"readResults\": [\n",
      "            {\n",
      "                \"page\": 1,\n",
      "                \"angle\": -0.0158,\n",
      "                \"width\": 578,\n",
      "                \"height\": 235,\n",
      "                \"unit\": \"pixel\",\n",
      "                \"lines\": [\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            1,\n",
      "                            7,\n",
      "                            545,\n",
      "                            5,\n",
      "                            545,\n",
      "                            32,\n",
      "                            1,\n",
      "                            35\n",
      "                        ],\n",
      "                        \"text\": \"7, Excuse me, Amy. Can I use your pencil? No problem.\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    2,\n",
      "                                    8,\n",
      "                                    15,\n",
      "                                    8,\n",
      "                                    16,\n",
      "                                    34,\n",
      "                                    3,\n",
      "                                    34\n",
      "                                ],\n",
      "                                \"text\": \"7,\",\n",
      "                                \"confidence\": 0.652\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    20,\n",
      "                                    8,\n",
      "                                    89,\n",
      "                                    8,\n",
      "                                    90,\n",
      "                                    34,\n",
      "                                    21,\n",
      "                                    34\n",
      "                                ],\n",
      "                                \"text\": \"Excuse\",\n",
      "                                \"confidence\": 0.942\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    94,\n",
      "                                    8,\n",
      "                                    128,\n",
      "                                    8,\n",
      "                                    128,\n",
      "                                    35,\n",
      "                                    95,\n",
      "                                    34\n",
      "                                ],\n",
      "                                \"text\": \"me,\",\n",
      "                                \"confidence\": 0.978\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    133,\n",
      "                                    8,\n",
      "                                    187,\n",
      "                                    8,\n",
      "                                    187,\n",
      "                                    35,\n",
      "                                    134,\n",
      "                                    35\n",
      "                                ],\n",
      "                                \"text\": \"Amy.\",\n",
      "                                \"confidence\": 0.816\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    192,\n",
      "                                    8,\n",
      "                                    227,\n",
      "                                    7,\n",
      "                                    227,\n",
      "                                    35,\n",
      "                                    192,\n",
      "                                    35\n",
      "                                ],\n",
      "                                \"text\": \"Can\",\n",
      "                                \"confidence\": 0.945\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    232,\n",
      "                                    7,\n",
      "                                    252,\n",
      "                                    7,\n",
      "                                    253,\n",
      "                                    35,\n",
      "                                    233,\n",
      "                                    35\n",
      "                                ],\n",
      "                                \"text\": \"I\",\n",
      "                                \"confidence\": 0.979\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    257,\n",
      "                                    7,\n",
      "                                    293,\n",
      "                                    7,\n",
      "                                    293,\n",
      "                                    34,\n",
      "                                    258,\n",
      "                                    34\n",
      "                                ],\n",
      "                                \"text\": \"use\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    298,\n",
      "                                    7,\n",
      "                                    343,\n",
      "                                    7,\n",
      "                                    343,\n",
      "                                    34,\n",
      "                                    298,\n",
      "                                    34\n",
      "                                ],\n",
      "                                \"text\": \"your\",\n",
      "                                \"confidence\": 0.977\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    348,\n",
      "                                    7,\n",
      "                                    427,\n",
      "                                    6,\n",
      "                                    427,\n",
      "                                    33,\n",
      "                                    348,\n",
      "                                    34\n",
      "                                ],\n",
      "                                \"text\": \"pencil?\",\n",
      "                                \"confidence\": 0.694\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    442,\n",
      "                                    6,\n",
      "                                    467,\n",
      "                                    6,\n",
      "                                    467,\n",
      "                                    33,\n",
      "                                    442,\n",
      "                                    33\n",
      "                                ],\n",
      "                                \"text\": \"No\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    472,\n",
      "                                    6,\n",
      "                                    545,\n",
      "                                    5,\n",
      "                                    545,\n",
      "                                    32,\n",
      "                                    472,\n",
      "                                    33\n",
      "                                ],\n",
      "                                \"text\": \"problem.\",\n",
      "                                \"confidence\": 0.761\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            13,\n",
      "                            66,\n",
      "                            516,\n",
      "                            63,\n",
      "                            516,\n",
      "                            90,\n",
      "                            13,\n",
      "                            93\n",
      "                        ],\n",
      "                        \"text\": \"Where is my pencil ? Look, it's here , under your book.\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    22,\n",
      "                                    67,\n",
      "                                    66,\n",
      "                                    67,\n",
      "                                    66,\n",
      "                                    93,\n",
      "                                    22,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"Where\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    71,\n",
      "                                    67,\n",
      "                                    99,\n",
      "                                    67,\n",
      "                                    100,\n",
      "                                    93,\n",
      "                                    71,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"is\",\n",
      "                                \"confidence\": 0.98\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    105,\n",
      "                                    67,\n",
      "                                    132,\n",
      "                                    67,\n",
      "                                    132,\n",
      "                                    93,\n",
      "                                    105,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"my\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    137,\n",
      "                                    66,\n",
      "                                    179,\n",
      "                                    66,\n",
      "                                    179,\n",
      "                                    93,\n",
      "                                    137,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"pencil\",\n",
      "                                \"confidence\": 0.974\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    184,\n",
      "                                    66,\n",
      "                                    201,\n",
      "                                    66,\n",
      "                                    201,\n",
      "                                    93,\n",
      "                                    184,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"?\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    206,\n",
      "                                    66,\n",
      "                                    257,\n",
      "                                    66,\n",
      "                                    257,\n",
      "                                    92,\n",
      "                                    206,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"Look,\",\n",
      "                                \"confidence\": 0.924\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    262,\n",
      "                                    66,\n",
      "                                    297,\n",
      "                                    65,\n",
      "                                    297,\n",
      "                                    92,\n",
      "                                    262,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \"it's\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    302,\n",
      "                                    65,\n",
      "                                    331,\n",
      "                                    65,\n",
      "                                    331,\n",
      "                                    92,\n",
      "                                    302,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \"here\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    336,\n",
      "                                    65,\n",
      "                                    360,\n",
      "                                    65,\n",
      "                                    360,\n",
      "                                    92,\n",
      "                                    336,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \",\",\n",
      "                                \"confidence\": 0.962\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    365,\n",
      "                                    65,\n",
      "                                    422,\n",
      "                                    64,\n",
      "                                    422,\n",
      "                                    92,\n",
      "                                    365,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \"under\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    427,\n",
      "                                    64,\n",
      "                                    470,\n",
      "                                    64,\n",
      "                                    470,\n",
      "                                    92,\n",
      "                                    427,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \"your\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    475,\n",
      "                                    64,\n",
      "                                    517,\n",
      "                                    64,\n",
      "                                    517,\n",
      "                                    91,\n",
      "                                    475,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \"book.\",\n",
      "                                \"confidence\": 0.873\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            31,\n",
      "                            101,\n",
      "                            126,\n",
      "                            101,\n",
      "                            126,\n",
      "                            120,\n",
      "                            31,\n",
      "                            120\n",
      "                        ],\n",
      "                        \"text\": \"65 40 %?\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    47,\n",
      "                                    102,\n",
      "                                    61,\n",
      "                                    102,\n",
      "                                    61,\n",
      "                                    120,\n",
      "                                    47,\n",
      "                                    120\n",
      "                                ],\n",
      "                                \"text\": \"65\",\n",
      "                                \"confidence\": 0.559\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    65,\n",
      "                                    102,\n",
      "                                    91,\n",
      "                                    102,\n",
      "                                    90,\n",
      "                                    120,\n",
      "                                    65,\n",
      "                                    120\n",
      "                                ],\n",
      "                                \"text\": \"40\",\n",
      "                                \"confidence\": 0.559\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    107,\n",
      "                                    102,\n",
      "                                    126,\n",
      "                                    103,\n",
      "                                    126,\n",
      "                                    120,\n",
      "                                    107,\n",
      "                                    120\n",
      "                                ],\n",
      "                                \"text\": \"%?\",\n",
      "                                \"confidence\": 0.954\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            27,\n",
      "                            121,\n",
      "                            315,\n",
      "                            120,\n",
      "                            315,\n",
      "                            148,\n",
      "                            27,\n",
      "                            149\n",
      "                        ],\n",
      "                        \"text\": \"Here you are ! Thank you !\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    35,\n",
      "                                    122,\n",
      "                                    84,\n",
      "                                    122,\n",
      "                                    84,\n",
      "                                    150,\n",
      "                                    35,\n",
      "                                    149\n",
      "                                ],\n",
      "                                \"text\": \"Here\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    90,\n",
      "                                    122,\n",
      "                                    128,\n",
      "                                    122,\n",
      "                                    128,\n",
      "                                    150,\n",
      "                                    89,\n",
      "                                    150\n",
      "                                ],\n",
      "                                \"text\": \"you\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    133,\n",
      "                                    122,\n",
      "                                    155,\n",
      "                                    122,\n",
      "                                    155,\n",
      "                                    150,\n",
      "                                    133,\n",
      "                                    150\n",
      "                                ],\n",
      "                                \"text\": \"are\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    161,\n",
      "                                    122,\n",
      "                                    190,\n",
      "                                    122,\n",
      "                                    190,\n",
      "                                    150,\n",
      "                                    161,\n",
      "                                    150\n",
      "                                ],\n",
      "                                \"text\": \"!\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    197,\n",
      "                                    122,\n",
      "                                    259,\n",
      "                                    122,\n",
      "                                    260,\n",
      "                                    149,\n",
      "                                    197,\n",
      "                                    150\n",
      "                                ],\n",
      "                                \"text\": \"Thank\",\n",
      "                                \"confidence\": 0.979\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    265,\n",
      "                                    122,\n",
      "                                    290,\n",
      "                                    121,\n",
      "                                    291,\n",
      "                                    149,\n",
      "                                    265,\n",
      "                                    149\n",
      "                                ],\n",
      "                                \"text\": \"you\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    296,\n",
      "                                    121,\n",
      "                                    314,\n",
      "                                    121,\n",
      "                                    314,\n",
      "                                    149,\n",
      "                                    296,\n",
      "                                    149\n",
      "                                ],\n",
      "                                \"text\": \"!\",\n",
      "                                \"confidence\": 0.981\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            221,\n",
      "                            157,\n",
      "                            264,\n",
      "                            158,\n",
      "                            263,\n",
      "                            173,\n",
      "                            221,\n",
      "                            174\n",
      "                        ],\n",
      "                        \"text\": \"iftiff !\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    221,\n",
      "                                    157,\n",
      "                                    250,\n",
      "                                    157,\n",
      "                                    250,\n",
      "                                    174,\n",
      "                                    221,\n",
      "                                    174\n",
      "                                ],\n",
      "                                \"text\": \"iftiff\",\n",
      "                                \"confidence\": 0.559\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    254,\n",
      "                                    157,\n",
      "                                    263,\n",
      "                                    157,\n",
      "                                    263,\n",
      "                                    174,\n",
      "                                    254,\n",
      "                                    174\n",
      "                                ],\n",
      "                                \"text\": \"!\",\n",
      "                                \"confidence\": 0.986\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            2,\n",
      "                            187,\n",
      "                            13,\n",
      "                            187,\n",
      "                            12,\n",
      "                            203,\n",
      "                            2,\n",
      "                            203\n",
      "                        ],\n",
      "                        \"text\": \"8\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    2,\n",
      "                                    187,\n",
      "                                    13,\n",
      "                                    187,\n",
      "                                    13,\n",
      "                                    203,\n",
      "                                    2,\n",
      "                                    203\n",
      "                                ],\n",
      "                                \"text\": \"8\",\n",
      "                                \"confidence\": 0.985\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            14,\n",
      "                            179,\n",
      "                            536,\n",
      "                            173,\n",
      "                            536,\n",
      "                            200,\n",
      "                            14,\n",
      "                            206\n",
      "                        ],\n",
      "                        \"text\": \"Where is my bag? In the desk? Not On the desk ? Yest\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    27,\n",
      "                                    179,\n",
      "                                    73,\n",
      "                                    178,\n",
      "                                    73,\n",
      "                                    206,\n",
      "                                    27,\n",
      "                                    206\n",
      "                                ],\n",
      "                                \"text\": \"Where\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    79,\n",
      "                                    178,\n",
      "                                    107,\n",
      "                                    178,\n",
      "                                    107,\n",
      "                                    205,\n",
      "                                    79,\n",
      "                                    206\n",
      "                                ],\n",
      "                                \"text\": \"is\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    112,\n",
      "                                    178,\n",
      "                                    137,\n",
      "                                    178,\n",
      "                                    137,\n",
      "                                    205,\n",
      "                                    112,\n",
      "                                    205\n",
      "                                ],\n",
      "                                \"text\": \"my\",\n",
      "                                \"confidence\": 0.954\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    143,\n",
      "                                    178,\n",
      "                                    189,\n",
      "                                    177,\n",
      "                                    189,\n",
      "                                    205,\n",
      "                                    143,\n",
      "                                    205\n",
      "                                ],\n",
      "                                \"text\": \"bag?\",\n",
      "                                \"confidence\": 0.745\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    194,\n",
      "                                    177,\n",
      "                                    216,\n",
      "                                    177,\n",
      "                                    216,\n",
      "                                    204,\n",
      "                                    194,\n",
      "                                    205\n",
      "                                ],\n",
      "                                \"text\": \"In\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    221,\n",
      "                                    177,\n",
      "                                    253,\n",
      "                                    176,\n",
      "                                    253,\n",
      "                                    204,\n",
      "                                    221,\n",
      "                                    204\n",
      "                                ],\n",
      "                                \"text\": \"the\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    258,\n",
      "                                    176,\n",
      "                                    315,\n",
      "                                    176,\n",
      "                                    315,\n",
      "                                    203,\n",
      "                                    258,\n",
      "                                    204\n",
      "                                ],\n",
      "                                \"text\": \"desk?\",\n",
      "                                \"confidence\": 0.901\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    320,\n",
      "                                    176,\n",
      "                                    358,\n",
      "                                    175,\n",
      "                                    358,\n",
      "                                    203,\n",
      "                                    320,\n",
      "                                    203\n",
      "                                ],\n",
      "                                \"text\": \"Not\",\n",
      "                                \"confidence\": 0.977\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    363,\n",
      "                                    175,\n",
      "                                    388,\n",
      "                                    175,\n",
      "                                    388,\n",
      "                                    202,\n",
      "                                    363,\n",
      "                                    203\n",
      "                                ],\n",
      "                                \"text\": \"On\",\n",
      "                                \"confidence\": 0.982\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    393,\n",
      "                                    175,\n",
      "                                    429,\n",
      "                                    175,\n",
      "                                    429,\n",
      "                                    202,\n",
      "                                    393,\n",
      "                                    202\n",
      "                                ],\n",
      "                                \"text\": \"the\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    434,\n",
      "                                    174,\n",
      "                                    468,\n",
      "                                    174,\n",
      "                                    468,\n",
      "                                    201,\n",
      "                                    434,\n",
      "                                    202\n",
      "                                ],\n",
      "                                \"text\": \"desk\",\n",
      "                                \"confidence\": 0.943\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    473,\n",
      "                                    174,\n",
      "                                    489,\n",
      "                                    174,\n",
      "                                    489,\n",
      "                                    201,\n",
      "                                    473,\n",
      "                                    201\n",
      "                                ],\n",
      "                                \"text\": \"?\",\n",
      "                                \"confidence\": 0.786\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    495,\n",
      "                                    174,\n",
      "                                    536,\n",
      "                                    173,\n",
      "                                    536,\n",
      "                                    201,\n",
      "                                    495,\n",
      "                                    201\n",
      "                                ],\n",
      "                                \"text\": \"Yest\",\n",
      "                                \"confidence\": 0.875\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            554,\n",
      "                            214,\n",
      "                            573,\n",
      "                            215,\n",
      "                            573,\n",
      "                            231,\n",
      "                            555,\n",
      "                            231\n",
      "                        ],\n",
      "                        \"text\": \"11\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    554,\n",
      "                                    214,\n",
      "                                    572,\n",
      "                                    214,\n",
      "                                    572,\n",
      "                                    231,\n",
      "                                    554,\n",
      "                                    230\n",
      "                                ],\n",
      "                                \"text\": \"11\",\n",
      "                                \"confidence\": 0.986\n",
      "                            }\n",
      "                        ]\n",
      "                    }\n",
      "                ]\n",
      "            }\n",
      "        ]\n",
      "    }\n",
      "}\n"
     ]
    },
    {
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zcBfEN7I1OojbBUPaN/IrpVfCMWUacaN3/TwufxGIb+3yo4oRGG2Lq3zyE9tdpiVSnpjxYXv33iqGDh3M6rUbUJ6KMTFAL3bFN95FZL8BWvll0886bxPxJyaU6jSIPy1giNDWFmJTRQWDBgygIC8XT+ndbEUFeVjioZSLcl0s00A6oSiy3zJcpx11ohimoXfqiaK+pobBgweRbJkoz/VJtSawSmLGzd5+0pG6+nqef+45+vTpw+DBg3jxxZdYvuwzhg8f3nmhO9TPkSlPjmACjauiD48vk0gpwDGEhx58kB9cfQ3Z2dk89fzzzDj55IPCLpw/n+uvuYaI3x0OJBIu4Ije3WcAbW6UxatWUt67D5kFeYghbN2zm8K8AmzDxBOIhMMs/mwpPXv2pLxfX7bv2UPU30RhdpQwxv/pULa4qjvWZnon6ObNmygtLUNMvYPTdRyU51FZuYmi/AKSg8H49yFKL0Kixv5iGc+XtrqeLs/27dvwIi7du3XF8Ex/UaP8jSAd68DDw8aUAFEFDWGPVRsr6VXeE2UHiAK2wO7qWlIyczEDSUSVXjjoncAdyiwH1/GB8Pznrujdl521i6E8DPfg7yO+EDPMTnbeJtARSqltRxi0Fr0Z6lnlV7B/Msd8YBRwFlpVdaSoAE4QkeOVUu99hfcS+D+Mb9XXGHUcolEH5U/AntJi/88/X8WiRZ9SWlrKiSfOwPUg7Cq2760hK7+QsNLTsiAYGESVokV5hEWIKA/X9fRWaxFcOkhpPBelHFwviudGUZ4DykU5UaKOh6sMosrgmut+gGEEuP7GHxHBwMEg6goOJg8/9ndMM8i0aTOIevi0zSTsGfz5L48wbvxEsrJzSElNo095Py6bNZu1a9fhOh5O1OOSSy7DtAJUbN6O6xkoZeBFAddk3txPsOwk7rjjLp+06S37GzZu4srLL6d/nz4UZ2fyP985mdOmT+XBP9/Lzr1VuMpDGYLjeSiBp/71FJOnTiEnN5uklCT6DezPrXfeQXMohCMKB4Xr/8W2tCuliKJoc10iKC669FIMy2TD1i388YH7GTB4EMmpKfTq1Ytf3vUr2lzYuKOKBYsWc8bMU8nJSKdrSRE3XH8djeEIjmHhmgGihkWbC20erFq3kQsvmUWXrj1JSkqjqKiYc887n89Xr8FxPaKeh9g2jpi0eYr6kEMoEiU9PRPHU3gYKMMk4hlEPJOwa+Eom4gyCXnQ5ipCLvz9yX/S1tbGBRdexAUXXgTAQ488SliJzoujiCjdp96fMxcRk5//4jY+XfIZJ0yfQVZmNjnZuZx++lls3rwNx4X1Gyv57tnnUlBQSEpKGpMmT2XJ0pW0eaL/lHDWOedhGBZvz5lHRAlRTxFB0ex6tKB48vnnMExh1tVXUe+6tKBoVh7Nnkez5+q6Vx4h16Up4tDsuDQ5il31jfz8x/r4ykf++RQTps0gFPVoCzk4jocTdQhFHAYfdQxvzZtPW8SlJRShNRzhhedf4OLz/4fB5eUUpaVRlp7BpOHH8Off3Us4FGHMmPH06NGTllCE5lCY235yM2OGDGBdxUYeuf9+xh89nB9cchEXnHoy11w2i9bWMMl2EhHPoykcoTkcpqEtTIvj0BgKUdvSRl1LG03hCK2OXpiEHJfaqEN1KMyWqr3sa2omLSuL1tZWmkJh9jRH2VHfxEuvv8OYwUO55ZZfsKOukapIhO0tLWxvamFXdQvl3brTp2t3du9toao2TE2zw7bqJm6/6x6OmzSFUUcPIzcjg17duzFj+kk88/wbbN7dyOpNVayp3EflzkbmLlrF2d+9mG4lXchNTmJIWRGP3f0bPvvkMzZU7qWqzWVZ5V6WrNpIq2fR5His3ryXlRt38vbHS/ntnx8myRTOOOscPlqzjfeXbeSdxWuYu2IjH69Yz+tzF1KYlUVxTg5LVq0lFNa7AzZv3cqPbryBof37k5uaQkF6OuNGjOAv99/Hrqoq1m/ZzNrKTWzcupVnX3qRoB3ge9ddx4YtW6hva8WBOOsWkS0isqXjWCoiF4uI8n+ni8hcEWkQ+XKZp4g87r/bU0RuEJF1IhISkR0i8nsRyTjEe2Uicp+IVIpIWERqRORVERnRSdjb/DQmiciZIrJYRFpFpFZEnhaRTh04i0iOiNwlIl/44RtEZKWI/K+IpHYIN/dIyqqUWqWUekZ1UIMopVxgsX/5VX10/RS9hLjHVxseEURkuIi8ICJVft1tFZEHRKT4K8Qxya/T20RkjIi879dPk4i8IyLHdPJOx3Y4T0Q+FZHmjv1JRIpF5H6/n0VEZJ+IvCgih12JishJIrJARFpEpE5EnheRPkdaHj+OUf57e/y0t4vIQyJS0knYuX5ZbBH5hYhs8vvtOhGZ3SHclSKySkTa/D59+1dpq87wrZBIxbqwYRh6i7pvY2QZFkopHnr4IQAuvvgiPMPCMwyqaxtZX7mdnPwc9tQ2EjCFZMNgUK8eeEk2jtiImBhKEFere5QrmjD48TsKIkqrykzD0LZUSrQdDwZt4QhWMMAv776HRQsXcd8f/8SEyVOZNu0EAqbJqi/WcON111NYWMijf38c0OQl6nqcetLJfPjB+5R16cJZZ59DVmYmm7ds4ZVXXmHUmHF079MXw9+SDxB2PaIimMpX44kR9xvkKUXU07uIdu+p4tixY2lsbGTqtBM4atRo0tPS2bljOx+/9zY7du+iuDAPw3dwdOXsy/jH449TWlrGKaedRkZGBp8tWcIdt93Khx/O4ZU33sS2bC0lMIRIOEIwEEApD9d3AeB4nvY7BPz4hz/k43nzmH7SSUyeOpW3Xn+d237xC+qb26htaOLZvz/KqTNnMnr0KD78cC6P/OUBoq7DXb+5G89T2LZNMGDz6ssvc8UlF+FEo0ybMYOevXqzbetWXn7xJd58401ee+N1epeXI4YFpoVYNhsqtuC4LqG2VoKGQXVNHZZhYAgkpSTT0tZG1Hd5kJqaRnNzE02NTfzl/gcwDINxEycRDCaTm5fH0089xcVXXIkdCCAipKWk4jgOy1Z+AcCHc+fx23vu4ahjjmHm6adTsXEjL7/0IsuWLuVP9/2Fiy88j97l5Uw87njqamr58P13mT7teJ57421S0jOorq1hyomn8MKzz/LHP/wBK5hKXnYOaVmZ7N63jzYnyl/uvx+A9KISXnrzLQoL8inv04eWxgbwPEyBvNw89u6pQgybtnCUgqJi7vrVL2luaqJLz14Es/Op2LyDoGXT2txMS2MDO3fupqRrGXYwQH5+Ps0797Bs2TKSkpK488c/xrJtepX3Z/ioseyrqqJi3Rp+dtMPmffhXK74wQ/p2bsXbaEQ9fX1tLS2AnDLDTey4KO5TJx8HMeMGctnixfx6tP/YtO6dRw3ehySFqS+sREnGsWybFJSkmlsbALADtgkJyXR3KzjaguFaWhoojUUZtHiJXTp0hUv6uK4Lm1NbdTVNrJx42aSk7RmZueu3Tz/4qt069qVnj17kJqcTND3fQUKFQ0RjYBhJPOD2Zfw7msvU1DWhTGTp+n0TWHFksX888nHaVVCW1sb9fX1JJmKO370fZyow/ip0+jTrz/r1qxm0dwP+PPPbsRpbeHq711NWloqSrn07tUN27bIL8hi3rwFZGdlMHbSRIpKy5jz9mt8Z/b3Wbp8BRkZ6RTkZpOdmc7niz6hqaGBS6++loH9y9m2xqYpCjd/70EWLVxN3379uPKqq2lpbeXlF1/kh9/7Pnt27uS2O+/0yyds27IFgLy8PHr26IlpGEQdB9s6ouH7TLSa6S3gQaD7EQ7LAL8HJgDPAq8AJwDXA8eKyHilVCgWUESOBt5F2xi9A7wI5AGnAZ+IyHf8DUgH4mrgVOBVYB5aCnQ2MFREhiml4vtURbvT+RDoBiwF/oIWBpQDP/DL1/IVytcpRCTLz5MCvqpUaTnwJHAB2h7qS6VZInIy8AJ6jf88sBUYDlwFzBSRcV/RCHsUcDPwPnA/0Bs4HZggItOUUh938s6N6LNxX0PXcaaftx7AJ0AJMAf4F9pH5FnASSJyhlLq9U7iOx2YgVaXzgWGAWcAk0VkrFJq/ZcVQkQuAR5Bb3B+FX1aSh9gFnCKiIw+hOTxab8O3gSi6G/gYRGJAkPQ7fI68AG6nX+BVun+5svydCh8K4hUTERUU1PDm28+QjgcIT8/j3PPPZe2tlb+9dRTGIbBid85i+rGZqJKWLxiDesqNqM2biYlJYm21lbKy8rISs4gOS+LxrZmolGXVMMkMxDAiWrJS1NbEzv3am8u738wh7qWNmzLJBJ1tI8l1yM1JYXRo8fQq29fHOVSWFzMPfc/wMzjpjLrogv5y2N/p0e37px31umEQiHuf+BBApbNunXrMO0A9993Px9+8D6TjpvGj++8i3CkjdTUFFzH5cxLL6WpsYHt9bU01DdQ36q/+8UrV7Cnrpbs9AxSgkHSU1OpqqsFoLq+ju379pJk2Nx/333U1tZy009v4YRTTmXpys/pWd6X1rYwxxw3g8ZwmC8qN7F3bxVvv/oq/3j8cYaNGMFPfvlLQqEwoVCI0y+8iKcfe4xHHrifH/3oJi6dfQXJyUls27KVlKRkCgoLUJ5HMClAUjBINBqltUXn87MlS3j5jTcoLCwEhKuuvIIJx07gL3/6PbYd4JlXX2f44AG0NDUy+6qr+c6pM/nn3/5G30FD6NK1KzU1NRQWFHDt5bOwbZsbf/YLSruUYds2F86azaWzZ3HemWdy7VVX8+JrrxJ1PexgMqYdZOeunaQmJ+FEo7iOgxuJYNoWYlqYvhGycj2inkNtYwOvvPQSzQ0NbFi/jomTJ7Piiy8oKy3j5Jkz+fujj3LP3Xcz5OjhTJw4AcO0ePGFFzB88jr/44948NHHOOXUUzAU2KbFDT+4niee+DsXX3gel19zLcdOO4ENFRWcNG0GD/7pD/zhnv/lzddeYeK06Xz0yXzKy/vQrWdP5rz7Dr0HDuWaKy8nLTODnas+p7mxgUUff0RJ125EIlFS7SDbNm2mIDuL7l3LMEWwfcefgZIiLNumpr6ZjevX8dnCBQAMGzGK2ro62lpaGTvqGDbU17Jlx07ycrKpb6xnxIjhvPbG29TW1tKjRw8aGhr47YOPMHb8sTQ2NvDxx/PJz80hGAzwyJ/+wEvPPk2/o4bTf2A/SkqK2FBRQUa6Fj6sXbWSZUuXU9atK64J2/bu4YzpJ7J6xXJ2b9nA0aNHk5mehiHgeAoDKMjJjitwTUPIzcoEICM1lZKcPKpr68hLSeeYgQOxbE3euxYUk5uZQdWuXQwo762HB08xc/oJNDc301xfT//uZQRFxwlCt5IcPGD9pk289/orlHXvzovzPqI4v4DFi5dQVlZGdXU1C+fPx/VamDF9Ek1NzUwdPoxgMIk77r2Ho0aMYOCAAbzw4kuce9HF/OiqWbz6j0c49+zTycrKICfNpiArBdOA9Zsr6FmWz6BB/di5cw+nnfldHvzjvSz78A36HzWCMaNGMueD98kryWHRB28DcMWVs7GVi6Hg2Wdh0cLVTJ8+nRdffJlAIADA7T+7lTHjRvPb//0Np8w4iXHj9OaIgD9AWkpIwsBA8Iz91eCHwYnAiUqpt4/0hQ4Yhzac3gogIjcDz6EnyZuAO/37FppspQGTlVLzYhH4koMlwKMi0r0jMfIxHRjR0ThbRJ4CzkWfz/psh7BPoknUT5VSv+4YiYjkAV/FlqlTiEgOmhB2A36ulFr+NaK5BU007hSRZ5RSrYdJLw14HD0XT+pIckTkx8D/oh1XT/sK6U8HvqeUuq9DXDOBl4HHRKSvUgc5E5kCjOmkvA+iSdTPlFJ3dYjvAeAj4O8i0q0TO7JTgFM6kiwRuQ74A/AAMPVwBRCRcuAhYAt69+XODs+moAnuH9EbBQ5EV2CQUqreD/87YB16YVAPDInFJyK3odWxPxSR36kvdx/QKb4dqj1fItXU1ExBQQGlpaXs2rUbpeCFF16gvr6eSVOnUti1C1HDwLBMTMti6pQpXHDeOVx8/lnMuug8Jk6cQEpGJrv21jBv/qfMX7SEBZ8uY3dVA0mpydQ2NrN4+WrwV3JLFi3kwT/cy59/ew8P/fH3/OWPv+eh+/7IvXf/muUrlrOQcm4AACAASURBVLNi1Wo+mPsx23buone/AVx140001Ndz2y03c/OPb2Jz5Sa+//3rOW3mTFKTkykuKiYvP49//uMJkpOTufpHNzN3/nyWr13L7to6Fq5YwZY9e1i7dRurN1WSV1JCxPf83KV7NxpbWsjOz6OopISMnGzS/YknPT2D7Nw8MjOzyc/TkuaiwkK2bN1KUjBIY10dzU2NJCcF6d2nD1ZKKms3VfLCU//ENE2u+MnNbNq+k/zSUvKLizGSkrjxZ7eQkprK3A8+oN/A/nTp2pXGlha69e5Jrz696dO3D7279aC0qJjuXbqSkZ4OwC9+cSujjhlBz67d6NWtG+V9+nDqzJmEQyHOOv9CpkydTH5BAV27dqFfv36c8d2ziUajFBcVMHHyRALJSSxZtICmxkbGTJ5MSmY6o8eOJZicREZ6OtNOOIFLZ8/iiy++oKWpmS5lZRQXF5GVnUFycjLDBg+itKiQtJQkuncro2tpMYX5uaSlJFOYn0O3shK6de1CfWMje6r3sX61ljCdfs65tIXDjBk7mosvmwVA5fp1zLpiNn3796OichOjx42ltz95Dx8xgvMuuJC8zGzysrPJzszkwgu0WjAzK4sf3nwzm7dtZdjRR5FfmMcxxx4LwJbKTVTt28dFF1/A5CmTmHjCiUQjEfbs2kZeYQGt4VZ27dnJ0oWfoJRi3KTJnDTteE48bgqnnTid7mWlpAQCJNkmtgEByyQ1RW8icJ0owaCF4zuTGnvsWEaPGc2+2ho2bdnOjt27mTx1Ajn5uVgBC095bKqsoG//ck44YSpdupaQW1iA60VZtGQJAwb05dhjxzBy1DGcft65ADRUV5GdnY2LUF1bS0pKMgA333IzvXp2J2CALUJrYz2nfudUAJYvXYIlepODAQQM0RsL0PZFtvjWb/7uNFEKcV12b9tBYW4OAdMi2TIQ18OLRqnZV02SbRG0NcHIz82muDCPzPQUcB1MAUsc4jt5xUG5ESxTq6TT01LpVphHwBKUipKbm0nPHl0oLMln4oSx5OZl88aLz9HS3MzNt9/GjBnHE25roq2lgZSgwWmnncwFl15K5cYNbN20gZp9VRQV5CB4ONEw69d+gRttY8WyZVRX7WbW7MtISkrilX/9k/EjhpGXkYzphslMslg0/xMGDx3CgPI+BHzzrLfe0raf9979O5LsAAZgKCgqKOBnt/wMgL899hiGp/RfzLGCaAN35XjgHvEOwVe+JokC+GOMRAH4k+9N6D0Yl3YIdxLQC/hzRxLlv7MLuBsoovPJ80+d7HB7xP8dGbvhq5HGAivoRHKglKruKCH7OhCRTLSUYjhwq1Lq8Fu7DwGl1HY0YSgFbviS4DOBXOCZTiRFv0MTieNFpOtXyEIFmqx0zNMraIlfb7SB/YF4+EASJSJlaAK3Dd2GHeNbgJZO5aCJ9YGY04mk6j60Uf8UEen2JWW4CrCB6zqSKD/tOWgJ1Skikt7Juz+JkSg/fCVaqpYF3NkxPj/ca2jp6dc+D/jbIZHyUVpayoABxzBv3jxycnIwDOHRv+pv6qprrqY4L4eI0j6TUmyDQeVdyclM00afShtp1ze3Ed4XYsKk8bz++hsUlJTRvWcppmnR2NLEiBHDqFy7FIBzL5vFwFGjOWnGDHJzc5g7Zy4jjz6a4sJ8tmzZwZq1a5kydDCeYdAaauPoseM5+8ILeeaJJ9iwdg3jxo3nnt/8BsPUhs8pqamsXLWKxoYGho8cSUpGOh5wwgknELRstu/Yyaknn8i6jRWYhkE4EiHs20zU7a0mPTODrLR07aPGEAKWDUDAskm1kvBcl9NOP5M7b7+dH994AyPHjOWMs8+m31HD6NGnL/M++QQDRU5GJrge1Xv2kJOby8J336VqXzXV/iSfnJSE5zpYts2uHTsQ06ShoRFlGGTn5MR3ZSPK32FInOweM/wYrA6GrrZlUVSs1dVHHTNSH6cinu8F3MMwdRlaWlrYtWcPrvKoWK+lujX79rGzspInH/0rmyor+eyjj0lNTaFiw0YA1q1bS98BA/AA13FpaGggIzUVUZ4/+cR2PSpMS/v+ii2zMrIy6duvH4//8Y9kZGbSo08fqmtryczKov+gQXTt0YPPly9n/dq1DBoyhIWLP6WkuJiqXbsBGDFqlPZujuA6LsrwKCsrA2DI0KFEnCgYQrcePQh5Lnvq6gCorKyke/celBQV0RaOMPGEE3jmb3/ls/mfILbF2g0bSEtP46WnnyYjM5Nhw49h5NHDMRVkp6UjtqF3IirfS7lf8XbAorikkEBSEMvU0gjDMAhHQriey+Kln9G/bz8+X72GUFuICRPGUlNbR89ePRk/djSe51Lf0EiX0hJuueEG5n3wHtV798YljTG40Qh2wCYSdalraCAQ0O131NHHoBBcZeACBYWlDBuuTV/q6hvaXXMoFT9OCLTdoghxr+kAIgYt4RDbdu1g5KiRGLZBxPMQy6C2sZZ9rU0MP2oYSxfOByAtIx1XYPe+KvKLC3BFwPebBeBi4BpCUWkZk46bytz3P2DKiFFMPXEG2fmFHD30KBoamiguLKaosAiUYuHHes5a+8UXrP5iDenpaTzb0oLyPDZ88QVrVq/RZautJTkjk/7lfVAiNLaFMYNJpGZm6uNacnJoaGjguBNP5PUXX2TXti2EWgvp3bsPzzz1FABnn38Bhh3E8xRNzVF27oTi4hzKB/THVR3O7jOESZOnALB8xQo8w6+7WOP4xuyuIb407oiw+MuDHBLzDryhlKoUke1AdxHJ8ieiMf7jbv4K/0DE7GL6c7Bvwc7OBosdeJ/d4d5o//edTqQp/ynchVZB/Uopdce/GdevgcuAH4nII0qpvYcId7T/O+fAB75j64/Q6tij0ITmSPDxIepoLtpFw1Ec3Lad9ZOjOsQX7eT5HLTbiKOAJw541lnfcUXkEzTpPgqtwjwUYn1qonRiYwcUoJ2ElKPVvB3RWZ+KHVF3YFjQp6+APsrucHk6JL5VRMo0TVzXZfPmLYwdO4Y1q1ezcOFCSkvLOHHGNEwVJUlMmpobSTYdcjNS9SpPaRNvF8EKGPToVUZtcyPBZIMRI4dhJ1m0trYSijRRWtYvrr5JSwoyoFc3uhYXsHXLFpIMj6LcLCylyMvNxg4GUSi6di1jwaJFtIXaGDBsODyh+8xV116LiInntrs6aGhoAKC4tIzkYIBhg4fQq7iEj+cvZOxRR1OQkcmalhD9Bg9k07oKwm16EZUeTGHsUSNIsm29E9tVmDHbMQW2B1FMunTtwrz5C/nhjTew5NNFLPj4IwDKunTh5DPOYlDfPgSSUqA1jFKKmupqnnjoQQAOtSz1XI/PV31BfoGeqFH6njLQO/06jNlZWZkd3FPoY2RM33VDRmYmIhAOhamt3kdGTh6NTVri29rSwqeLF9O9e3fqfdKxdMECli5YcMj+0OyfN+YqRSgUwjBMrIDtnwnoYRqWPq5FO0GKu3JwlWL16tVsq6igtaWFcy68kDffeZsLzv8f7Z4iEOTKa6/lpzfeyN2//CX3P/YYGRkZTJk6lR2bNvFbICs7R7uaUArD1Co229afS0ZGJnV1deTm5hIMBFi2cgU7du2Ml7NLWRmiPD5fuYK0tHSOGX8s899/jzdefZnVFRWkmhZVe/Yw87vfpaykiMy0ZFRUe7OOEcO4cwrlu4EwTTxPsX3nDgqKCgHYvnkLSxYvobx3L+bPX4DrROjWtZSioiKCAZt169bQq2c3AsEATY2NhFqbOXPacWzbsoUBQ4Yw9fhpZGdnY1oWW7Zs5bknnyDU1kYoHNHHkLhe3KDZTE4nioESCLlQ3dRGKKr7QdTxUEqr2lS8LWK+nvwduF47IfBQrN+yCUmyScvOJOLvgI16Hvsaa8kuKKKopIB9jXpR6QnUNNWxu6aKbj274wqEVbvTyhAGrgGuKcy+4SbKBw7mndde5U+/0Yvo3/zsFsZMmMTP7vwVAdNCuR5NDY0A/OPRRw/Z/wDCkQgNLWEaW8O0RmvJys4kPTefgtIuOFGHFWs2kJebzeQTT+X1F1/kicce4/zZ19KtRy+efvJJMrOyOPWsc3ARNm/dzq7duu/nF2QRkfYjc5Tf5jnFum3rG+qJiIcCoqJrLqoULW5U2y0qRdKR2cfuOZJAh8ChJv89aNVXJlpVkuvfP+tL4kvr5F59J/di6pWO+sss/3cn3xwm+7+/+3cjUko1isjtaCnMbWgJS2fI9H93H+J57H7WIZ53hsO1W8c0O3vWEf9O3r5OHjoi1qdu+pJwB/UppVRDJ+Fifepwz+wvSeuQ+HYQqfhKFUKhEFVVVRQUFHDHHXpRcM6556BcF2XoU/Rqq/dRVJiPSUw3qV0VGIZNMGARdRSLlywCPJKCAZqaGmlrbcW2TSxTEH+17LkOPcrK8MIh1q1eRa8ePQmaBnguaakpDB08iGWff04wOYmmxkaSgkncf/evSU5JQYCbbvgBx02aTH5BPkpp/1QZmbp/7Ny+jbVr13Pc5Im4rW201TXS/5gRNNfWY3uQn5nF53UrKczNA6B7cQniOoiyaWxqJhgwqa+vj1ePKOKTem5+PrOuuoZ/PPMsWyor+OC993no/vt48A/3kp+bw1nfPZfuXbQkuP/AQdxy26+YMWMa+/bVsmnTJsaMG03Fjq3s3rWb0SNHULGpkjWr13DGGWfgRj3Wr19PS3Mz44Yfjes6mJbpn6emd1a6nodh6nPmNInSz/ZWVVFb18Cuyo1kpKWQlVeA4x86u3nzZvLLyhgycCDpvt3NC2+/zZTjj6dy82Zqa2oZMXQoAdsGpZ0kGoZB1PEIhyPs2LmHaDTCrl27iYTD5GVlkZ+dqz1qmwau5+F4HoZlEgmFqK+t5a2XXgLgaZ/4Pvibg20JP5kzB/E8MtLSycnIZI+pP4mamhoikTC2ZWMaBk60XXWuUGzcWEFBTg6fr1zJqnXryMvXKlfleaQFA+zctpN9e/bRf1B/Rk+awvz33+P+393LjO+exevPPAPA0BEjGDJ4iC/FAZQ+ONqX4/i+yLRX/LDj0hqJsmnLNiZPPZ4PXn+dt197jZlnn0e3rmVsrSxm0KCBiMCaNesozM+ldl8NwwYNJijChh07mff222zbsoXzL72Msy+6hH59+1G1p4qePbvz5D/+wXNPPkF9fQNuJErYUzQ1NFK1V58m5yqormsmEDRwDJP1FZvZvluPiRHHJRKJ0hTWKkfDMAgEbMLhSPx8OMuyaIvoRUMoEqaqZh+9+vSivrmRqOsRDARAhJIupZSUdaU1HMK29TxaXVPNgkULKe/dG9eNUl1XTRJBGhoaSEtPZ1dNvXa14XlU1zVx9PgpHD12MqV5uWxav5Znn3qSD999m7rqal595wPCEYdU37nobx/8GxOOn0ZrayuLFn1K/wH9teQzI4OePbqTlpbKJ/MXsOjTzygrK2No5mA8V/HRRwvwPI+SkhKCwRQKS7tS3n8grzz3HGdecBmvv/wytTU1nHzG2WzetpPmlhD19Q2kZuj+tXdPLRVbNyNo1X19Qz0iwh5fIhpMSmLNxg2YpklVjbbp3FO1l607d1BYWEhqMKljN86k8wlCd9evj0KgM6PgIv+34YDfmUqpV/+N9A6HGOH62uqXI0Bs11/jfyi+h4DvA7NF5E+HCBOru6JDPC8+INyRoPAQ9w9st47orJ/8O3n7OnnoLO1MpdR/qj2+MXw7iJTfhJ6n2Lp1M9nZWSQnJ/PUP5/EMAx2797L3HnzmXL88YQdl2279tGtew/2NbRgGmD53odN00Ysm2WrPseUAPk5haxdvZ6s5FSys7KJtEWp2VdPbZ1uI+1iwWDVmvU0N4dAbHbvqcaNRskpyCclycY0FJFwCMsUHvnd3VTt2cPP77oLJxLl17ffxoUXX8Rrr78B/uHDAwYMICsri7VfrKaptpaczHSqd1eRn5dNUtBi754mUpICeK5DdmY6SclBABYuWcyQ6HB6l/di3oIFZGVlsmjxEp1PT+E4HmIJ4XCUFStXUZBfQEZ6OoOHDmPQ0GEMHzGSk4+fypx332Xs+An0Ke9J/wEDqNiwnry8TAwDItE2WtuaaG1tpra6Ftu02b1rL8uXf05SMInMtDSU69LWEqKtJURLuI2kQJBoNELUt+WKKo+2aAQ3pM8cMx1FNKrJUkNDEx9/vICh/XpSUlyA4zikpOixKWAHGHHU0axcvpKefbSkf8WSpRx77EQChsW2zVuwEAYN6B8/lLi5uZmt23ew4NMltIYcGpqbqdy0jqAdYPiwozhq8GDt3h1ISU3BRdFU00zEc9m4Zi1rVq0iPSOD4SNHEQqHCLeFKS4upqm5Cdd12bRxI3t37+Kpvz1ORm4e7779Drt3aK1CJBwmEo5SWVGBJZpINTfpXWiN9Y3s3bWb1WvWYgWCpBfmxyUcKHj/nQ+oqa5h0NBBRENRcnLzKOveneVLlnDOhReydNGn9B04kNy8XILBABsqNuGEwhgipGdn0drSCrikpCSTlp7BrqoqWsMR6ppaWbl6Ld1LikhNS6Ni3Vp2b6kkKy0V2zT46MO5uJ5LakoKpnLJSk+jramJaqCtpYXaKr1IPPnUmdTXVPPZp5+SlppGSWEha1Zo8whDAFfRVN9A97Iygr5qr6G+AdOBYDBIam4W/fr0onan1jREQmFaw1EaGhoREWw7QLph0NjcqneX2RZpaWm4Phn1PJch/QZqKVQ4jCEGTiSCaZgMHzwEw7Lx2toY0Fv3kx2Vmxnatx+lRcU0NTeTbNlsqdhIQ309aenpmL4ksqG2li5FRQwo70XAtslKTeOYoUM468wzGT96OCuWfUZrYz0ZGVmMHzeOOe+9TbS5juLsdLzMVJLHjiQQsMnt1wvTsuNOdaeOHal9llk2lqGYNGq4VlsbBmLqo2eOP3YM1ddey/XXXEXF55/x9muvIiL84tZf0K17T2zbJOopdmzMpqQEdu+ux2kL07tPH0zTJDdLL+rnz/kQgJGjRlHesxciQsTf7RhubaNn127+MT3xeS+Ilgh8lYn2SDERbVAch4j0RO/a2tLBDmWR/3ss2nblm0AsjRNE5KffkHrvN2hS+nWOijwIvmrux+ida3cDNZ0Ei9klTQL2E4/6Rvzj/ctlXyHp8SJidFJHkw5I88sQCzdeRKxODLFjErzO8jbxwBui/XPFyvNleViEtlU7FnjjyLL738O3g0j5EIGGhkbGjRvPs88+Q11dHdNnzOD888+nujHEjn0t7Ny9h883bGP99qr4MSq/vulaavdVcc/9f2XE0aOp2l1Hn74DaG5oZPOmbRw3fhxtLW1U7a5n374lYPh2O21RPl32BWVlZRR37c3CJSsZ0LcfddU1pKVvpr6xnrATJXfoYN584QXmvvceI8eNo0vPnhw1ZCgrli7lrddf4+e/+DnXfu8anEiEcCTKOeedz4MP3M9L/3yCSRPG4ThRSruWsGvvbvbs28uHH31IVl4WWXlZZObmAPDUv55k2JixrK6sZNu+vezat4cH7tOLmJqGevY11OMqYeGiBbS0tNGrV2/q6xtobNIT+KYKbVeUlJJCBBfPFi664gp+ct11/OrO27nzt/dQVFRMZn42FVu3YJsBtu3YyuqVKykoLMa1o2zfuoO83Dxys3PJy8llT1UVKSnJOFEnvg2+obGR5Lpa2trasCyLFNvC9V0jdOtWxpDB/SnNz6G2Zh+eYZPrly83O4duZWVkpaczfsxoHn/oQe7/w70MHDKY0aPHMHjQQIKBAPX19QQsg2VLPmPQ4EGYpsmUKVOoq61jwoijKS4p5cMlS8hKT2ff3mqUEyVgB3CUh4eiqbUVEdi1uRKAiy6dxeyrryZg2yjX1V7QlfZRtXjxImZd8D+88sLzvPr2uzQ3N/PBu+8AkJWRSXIgQF5hAeIpiDlzBFJTUzj1pJMJpiTT3NrGmx9/xIjh2tQhLy+XUcOHk5ubgx1MwjM9evbuSV5ygCsuvojf+dvar7/phxw/40QKc3Opq6mjatdOMjKzKS4pidsXGYagDKFnRgZhD7bt3M2AwYM59uihJP35Pq645GKuu3wWD//9CWaeNIOwf/ZdMGBhCSxeuIiLz/ku8z9bSlFhAePGj+f9t99i++ZNXHrFlYhnkBy0WL5sOS88o+158nJyyMlMIyM1lZ5dS3jmb77EtDCP8h49cJUirFyK7Ex2lepFZ2ZaMlmZaaSnpyKGlqqJCMnJKfhO+FEK0n1SnRpMpig7W593aOqjZbyYh/XMLO0PTqAkI0u761i4kNxgChlWgKycXNpaW7nrtp8DYBoGpbnZRD3FhpXLCTc2UZIzkqBlIYYiI9mmsVFLpC3LIj8rjfSMZK68/DL+9Iffce9vfs2xY0YxcuQoSjLTcFwHw98RN2/eXCZNnkI45GFaFqZp6pMKLG3Ir5TCtgyiUW34fvF53+XWn/6E+35/L7t27eL4449naP8+uL7qP8nQpxrMmAGPPqq49eabeebZ57AMEMOiqmofd9/1KwAuvfgSkk0LFAzs24+MjAzeePVVanbvpbhIL+rb9AnXXf69UfewuE5Enuiway/mtdtg/239r6CNiK8RkQ87c3MgImOAlYfbwXY4KKWWisgCtMH5j9E2SB3jzwVa/k2D83fR6h0LvW3+34ZS6mUR+Rg4GW0EfiBeRjsFPVdE7ldKLerw7HqgJ/D+V3AwCtom7Wq0WhGI79qb6OehM/cHneV9h4i8h3aLcD3w2w7xjQLOA+rQRPFATBGRkw8wOL8WbR/1YcdNDIfAfcDlwO9FZKNSakPHhyISAEYdwpXDfwQi0gvdHzYdwkYsjm8VkTINkzGjR2NaBtdeo1XKs2fNokuXMsoz80kK2hTkZjPzpBkEkwP+okzx+5/p3T3jxoyivEd3+g7ojjItXMfD6t+XjCSbcCiVk4smI6bBhlXarm7Hlk1UrFzKltXL9f4fz2NnxRoERWZmBrOuvALLtlm+bBkvPvkkpV3KuPsP99KvTznJSck8/NhfmTB6DPfeczcnzpjO8OFHI2Lwy1/9inXr1jJ3zhzOOmkG0088kfT0dHZs386c99/ntrvuYkCfPjjK5c477mDO228x5713+N7si+g7cBBrVn/ByiVLmH7Kybzy/AtkZqaTmZuOeAZz33uHRx5+kPHHTqBnr15kZ2VRubmSt954g2AwyI9+8mNGjR2D47n0u/ZqKtau5a8PPshpxx3PlOOm0rV7d2prati2ZSvzP/6Y8y64kGO/dx3K9Sjv1RXbMinIS0OhJynBQIT4rr283FzKiktQnkIMwfA8Unw1Q1lhHr26d8dSLplpaUQ9RWGeJlKFeTn06NIVs7uBGMLTzz3P2WeewbmnzWTS5CkMGPj/sffecXJUZ7r/95wK3T05KYyykJAEJggQshIZE0U2TtcB23jXXtbh/mzveq+9ttdr+zoCm42N16yNjUkG2wQBBkkgQBEkoRxAOY4m93SoOufcP86p6tFICHBY63fvvPOpz3RXV+7qep/zvs/7vG8DKdizcxdLFr/IoUOH6Cn0YTGMYKuyYC2XyzK6dTgYaJxQjYptD0NtDAhBq4Cenh4e/+1v8X2fz33+swwZMhSMbdUjhW29ozFMPGEs3/jKl3lp+XIO7N7JKaecwo4TTgCgtqaKTOAzrKnZNkcUgj4XkQpDn+aGWowQZAKJKRcZP9IS7n3PY8KE0Qhj0AhiI8lKeOeNN/Klz3+OPbt309LSwrtufBeZTAZPCOY9+lv+4iMf4QMf/CA//s+7MFL2U7u3jX1DYdi/Zw8TR49gzIjhfOADHyQqlfjspz/FjVfN5bTTpzJ95kyamhrpOHSIxYsX88qqVbS0tJB0U/nAhz7Ev9z6fb7w2c+yaOFCJkyYyKtbt/LYo49w9bXX8eD99yEFSBPjCw1a264AgGf6UOUOjBD40qDLGk/Z66FVnqh0AIRxIMoqzNsOBdrxgDRxZFNUKu5CRdus0r0xaK3S5sJG256ScRzje4KP3Xw937/1LmacPZUrrzyHOIpZsHA5w1ubGT68GaOLdB56CqVitm1+nE/91b8yadJoTj11AiNGNtPTneep3y1n3742bv7Y5ZTKT1I6JBBCc8cPP8FHP3Ib58yZwzlzTubESa14UrJ79yFWrNhKR2cvWzb/i+WuiSRIYayQr7sudtBvEMKe63XXncxdd70IwHXXt7B757fsdVHKUhcKm3n3u2HNmpP47W9+w9lnnsnll19OX6GPBx54gAMHDvD5z32ec2efA8pW6uWCDJ/65Kf4+je+zozpZ3PtNdcSxzG/e/p3YHlECZH2j23PAyuFEPdiI16XAqdjCbtpFZcxJhJCXI/Vj3rUAZ6VWG2e0cDZWEDQ6ub9vvZ+LGH6m0KIG9xrgQUOlwBTsFVuv689jeV+jf8DtzPQPoeNsEwc+IExplcI8RGsrMRCIcT9WFL5Wdhz2gf85Vvc3zzg+0KIy4FVVHSkisBH32I07+PY++C7QohLsETuREdKY5sT9xxlvd8CDwkhHsKCt9OxUhztWJB3TDPGbHDX5T+BtUKIecAmLLAZg41UHcR+538qe9P3w3EBpNKiFSnIZkPWr1/P888/z6hRo7jmmmvwPA9jFBgYWd902LodnR3s27OH2bNnMWPqFMxhYrYVMmY26wM+wghCN/vFRc/x4qKjA9qxY8fyN3/zebq6uvjYBz4IwH0PPMD0abaAwBhDVUsz99zzc8495zw+9IH3s3zFYuob6ghDyWOPPsAdd9zJz+/+JffcfTfGGFpbh3HN1Zdz4ZzTCPQhfGPIZjXzHvs5f/t3/8j8pxfx0rKlTJw4jjv/83s0NtXx6wceBN1OXFyPNJJ33vB2ysV9LFm6ipUvL6dYLNHaOoQbrr+IW255HyedFKJ7lyHRxH2G73zj3Vx47ih+8pPfsvCZJ+nqytPQUMvIUUP4q1uu54Z3nkXbngeYT7fuVQAAIABJREFUMGE8xd6tFK22OWBAOaK50JSL2wDoavsN7bkhgHbOw1DoWwVAX/tjdO3fYh2iK03Pd1kyeU/n07Tv6wHncE6apHjssVv40Y8W8uyzK3l+0ULC0GPo0FqmTx/JpZeew85tX8P2N9QsemYDAJddOozdW79gW9e4djDWuemESMa9975Cb28vF188jkL3P7Cjx5UdJsdlEkevuWpuLf/8z/D9797I333hDHbtsLyftrb72bRhLWjtnKVm127rAzo7nmbdmrmAplgqckJjF+3bLaG/L7+S1avOd61/KvsBzcUXS+65By67zGPr2jkk7Wx2bbN6Ye2HfsPLy0+x9zqk34MxGqVjwmIB32R4eYkN8ZxxquYX99Tz4AO9LF++hl/evYpCwVBTIzjhBI+//ussV15ZZPmL9S4VpLntNsUddxieXfAwT86D0aPh05+Gs866jwfvh7YDP2PZop+lv4NDrmXripdPY/cAyulGW9jGrj23smzJrUf9HfW37fYWor39N6xY/uYyQFfOhe4eePTRNu76r4doaoILLoCbbtrLTTdBuQwb1lwKwPjRcNNNsGrVThYu3ElXF9TW2nP88E1w4YWPs3HD4+m2hw6BH/0I7r0Xli1by4uL1xIE0NwMp54G554LW7a+Hkf46DZ7Ntx1l93GpBPvYftRxt1BAPfe+zV+9rON3HPPPfzrv/0rvu9z6qmnceutt/He977XtYPCVuoZw5e/+lVy1VX8+M47+dGdP2L48OG8+93v5nvf+95mbAn6n8L+J1an52PYyrFDWO2eLw+M/BhjVgshTseW+88FPoz9ce7FpnG+ArT9IQdjjHnNCX/+DVbo86+x4GAbliD+phoM/3ebMWapA6PveZ3Pfy2EmI1VRb8Um17ch9Vw+kcnIfFWbAnwNazO119jweYzwBeNMcve4rG/Kqwi+pewQOh8LIdsHvCNY2zvV1j9qy9i5TEiN+/vBkaXjrHvu4UQq7BioRdggWUeO3B4ALj3rZzLn9JEpQLrz2cnn1xl/v3fC1RXn47nVZFW+tDfEVX+93+98Nku/ubzu7n11hHMnFWVOl76O8wBznPgNiAZNfd/fazl//zX7P81+7d/g0cegV/+EurfqN7jOLXPfAZWr7ZFn05J4Tg0kU42kyOw/SlF+jppxoRw/13+rvJaHrkNpCvzP3I5O18etrwQ0gkqHG179jit1IZEK01Pb5662nr8ILC6SwIkXmV54bmfbb/jSPpVin7nnJ6XKxhLzsvYbgPJsVqpB2GrG/ud00MPr+N/feEpPvGJGXz6M3Ps/oVdNjkXIQWNze8iWzUFG81ychGuQtZDgLKVkJXgpI32+b6P50m01gS+h5RyhTHmiNYff9AdIMRdWPXn8eatKWoP2p/RhBDnY1XJ/8EY89U/79H8v2XHRUQqsXx+1VteZ9lSmDABzjhjD5Yy8N9p/R3E4Y7gWI5IuPXsQ7qybv/ljlj3DZzTEY7osHUq2zAG+voKZKuqAEFHRxc11TVUufeCw8/JOEdneYKQOKDDHa7ntg9OM8FWmwnh1j/82IwR/Y7Rs6sh8ITnJAeOXGf9+h/ynveM5bTT5oIQaGWditYMcHASpMAY6Zo1V85bCqt2BPYz3H6so5JpBaJtKARCeCgtQAq0se2L1m3YzPDhw6mprWHt+k14QcBZbzsZT0g86TsZAIkxgsDzMMbqcC1/aT2rVn2cCy+awWVzb0VI6ZroBk5jyV5T5fSRlNbs2X+A7u4eduzZTW1dA2ecNhUvCKkWfnrsNlprHXFsDKEXooEoitznAQLhJAoMYRDiBSHFYgmjIQgCAj+kr1iyKU+tyWWzlMtlym4bfjZLrBRRrDBGk81kMAYKhQIGu81MJqQ334tSisAPyOVyFPr6KEVWSqE6myMqlpBAFCuqcjm01kRxTBzHSCkJAp9CqUiMIgxCwjBDodhHHMcIIaivqaOvWCCKIpQqIv0MW7dtZ8++/bS2jmD8CVMQwqDKRQSK+kwtvvTTYgiw51soVB4UQS6kp5CnXC6TyWSoramlq6sTpWwxRa6qiqgYE/WC0or6ujq01sSxIo4jstksnufR29tLqVTmp3ddhu/7vPu9/0Kx1ERVVRVBYCuHAWqac2ih2XWwBxVvQUhBXV0d7e3t+L5PGAQ01TbQ22GPIQgCMpkM+XyeKIqora2lpaWFbDbzhz26Bm3QBu2PZscFkMpmxzF16h0MHJH2BwvKCEy/eQgrN/Cjnxw+gj2a4x4IUsxRRtJiwDLSvT4qMErAhXt3LNMpm6JiiXChSXkkBmGs5o7WlWiXEAbluEG+54O0I1Ht5AeEEERRbHk/0vJ/AMpl5aqnPOJYo5TCaI0f+BitWbR4CT0yQ2dHO02NjYyZNAVcj7/qbIZyuWwJtD7kslmU0hRKRTCGqlyOWNlyd9/3CDyr5VQuW4cZZjKEQUBfoYDRtrKpKpejUCqly1RXVVEsFm1Zf6lEdXU1KlYoVy6fy+VSKQylFGEYsnjJ39DT00sc215udXW1dHV1OaKvJYDHsSKfz1MsK5qaWygWi6mzz+VyGCHo6bHOvqqqijATWiFSramrr8cg6ezsRGurazRs2DD279mPNppYG1qGtKC84Sx4cQt+UGLosOmMHzWe/QdK+ELQUF9PoVCg5GQA6mpr+fndP2Pnju38+uGHkVLyV5/+X+w+0IhAEAQWcHR1dRHFMZlshuq6OvL5PIVikYOHMnhBNQhJQ8Notu2KaR0+hJ6ePKVSESEEmUwGow2lcgmtNbU1tQghKJVL1vHW1FJVlaOtrQNtDJlMhjCwoEdrTTabJZvNks/nXZTDp6q6mr6+PpRSaAPZmhqiOKZULIGA+ro6PN8n35e3kZKsQkoolUrp/RmEAbFWKBUDHkaA5/ugNb4PYRhaYrdnAZQxhkyYQWPwpSEThoRBaH8Txt5H2WwW6VutuVDWYaRHfW0Dp56sCcIQKSxJXWaz6LhETaYaIUTagsUYg+97+L7nClUEXhDghyGxUvieT+D7eI3N9jcKhL6PzhhkjX0fBDLVydIKfF/w/POLeHbhQp577lk2bFjHxz9xC2eddTraGHzfRsSam2uQErRnEJ6gpbEx1dkSQjK0qclFrwyh8Gmqq0Frje/5NupljIuDG/tseqMHz6AN2qD9t9lxAaSkrKKh4XDV+hRgCEE+n6evUMTqEnqEYUixUExHjVVVVfT1FSiUitZxV1WhjaG3t9eOKrNZCs4pG2NobKilu6cH7RoB19TU2BF4uUwcRzQ3NVPo7cYYQ7lcpqmp0aoS9/Skzsv3PdoPtdmedJkMnufR4wQkGxsbCcOQ3t5eylGEnwnJZrP09dnRdXLMySjT8zyaGprp6uqiz1XHtbS00NfXRz6fJwwzhGFIdU01Pb09aGVBUVVVFR0dHek1qa6upq+vSKlYxvd9amtrKBSKlEo22tBQX4+QkubmZjqLeYYOG8qYUaPo6OpCxwrf8xANDXS2d1KOIrxQUltXSxRF9Pb24nkeCEEcxxSLRXzPw5MemTCk6PaBEHieR+wiDWEY2qbHcYzW2kZrsJo/QmswFixKAZFWVjsqivA8q4yecK6T70pKDylt5EQI6ZwiJJA2DEM8KfB9iR/4ZEwG6b4zhKBeWmJ6GAT4vm9bbgC5bMZFcKwmlZSCXDZk9IgWojjG93yyVTnqzjyF6aefjPAkuUyWQqmIrwXCGPwgoLGxAaUUAkEmm+XHd/6Q3bt2MW78eL75ve9w+ZVXUipbqQMpJZ7n09TURBzHZEJLPo/qGkDAqSdORjuwHStlo3yeJGgZ5kjcVrMpEQ7VRuMJe321Vniu+sxgaG5qdiluS2DWieinGxDYqJhz7LjPXbldIniqndq6cNfaNDZSUb0yNNfUuFd2G1RX209F8pvG8dqEq+RL1ib9rdfU5FIQI4C6MJsk1tFaUx1mwBg8o9BI4sCKlSYUBWkMngCZyaDd4EsIZcGHFMSxIueiYb4TOQ2lhwghVvb6ZQMPKWxUUAiBkZogc+Sj0tU3sHDBM/zj175GU1MTN998M9/5zrfJZf30vLU2VrHefU9A+uRNzhtZAXfGGEQQ9NtPAp4G0dOgDdrxaMcFR2ratGlm+fKKqvvAYyoWi/T0FdEuUhQEPuVyhPXbVvwvimPK5TIGqKmpplyO0gd6GIYIYdMdxhiCMEBp4xyewfcDylGZTBhSjiIyYQZpbPjeqllLK0zpJWkz68Aw2iaC0uhQZCM7vnVgSWRDYysStatKMtjWKlEcpRwOS7+y4oXKAQ6lDZ6UFEslwsBPU1jG2PkIiKI4FT2U0kbkvH4tXBJ+hZAghS1NV8agfUGFl584M0BrIhc58jyB9Dy0Uo5HYlxpeKLELPDd8Rptr0NSFZc4oSRdZLRx35ewoAAboRDGoI1V8EZpfN9LUzn2nGR6PySOJknV+L5PoVAgk8mmpenGGIyUlOOIMAiIlUqvt9EGpQ2Z0EMbiGMX7fM9Ype20toQBL5LzxlMXLDpM21Q2soLaGNSsCg9D5RN+/m+n4L9OI6t41axizTY47WRTEMpiu0+tG1vo5QFNoGLOwghUEoRxwov8JAOIEkpwZP8qUISA58GwlhV/Tdc62iHM+AYFWDe4LCFsUr+h2/eztDGoOIYz/MQOsYIadOaQqC0Ba+B7yGNjQOXtEE41X1jTPp7lkKk4Bil8YWkXI7JZOzVF8L+HrXS+KFHsVQkW/WmmwQf21Sltc3rXwQwR1wEMWAt+06KPz5HatAGbdDemh0fTYsHWBKNSl5ns1maGutpaaynsb6G6qosTY11NDbU0VBXQzYTUldTzeaNG3j5pRWEjmtQW1VFbXWOTODbkWYYgjE8+eRTCKPJhgHr128Aowl9HykkmSBECvA8n2w2Q+D75PN5Dh1qZ8WKl+nL563jx+B5sgKalCYIAhsR8azj81zERgJGK3zpEfg+gefbUbWQBJ7n+DhYDR6jU6AmgGeffZ44ihHCKmz7nqxEGrRBxRGP/PYR+vJ5jDJW7d3RlTAGT0LgS9sfT9voQtaXZIEQTYA5rM2OlIKf3XMPz73wvI2aYEGgLyWB9JEuBRh6vgVRscLEdsQvjT0mrTQSgYegXCyB1qgosiDLJVI96WGUqogLuihQX1+BPXv2sHOnFcZMoohJ9EW7dKHn+ezcuYsdO3agtbIRGE/adJRWHNx/gHKpTNvBg7yycpVNKXmS0JfEkbL9+YQV/lRRjAR279rFa1u3gNYuSQy+F1AslFi6dBlaGVSsCbwAExt0rJHagntf+milWfXySkqlIgjBI48+xvadu+grltw9IUDbcwk9D09KyuUymzdu5qUVK1BRZK+15xGVIySSMAgIPR+hQTin72HBxp9i8gZOrgBDHHOqtLU51iTf5DRwveQ5oB3QLhYLxHEM2nCw7SDr16/nhRcWE8exjWIC2gGubdu20X6onSiKEUBb2yFWv7LG3qcupKS1QRvF9u072LFtJ1oZCoUim7dsYe+e/U4G5Njn9uYn84bT0YtZjkCXx3qEDtqgDdp/o/1BqT0hxDagBzvYjI0x04QQTdiyxHHYstR3GWM63uJ2bbqnX2TFS8LiwobKpahEKCQ22qGiMpvWrePSiy7ECy3vQiIoR2V8z0cISVFrVr20kvPPORejNPf+/JdUfeQmnn/+Bd7/P95LriqH0YbXXtvGr371ENlslmHDh9Hb08v9993HLX/9V1xxxWU2/aENUtrjlb50KSp7zAmvw2iFL2xkRSuNSUCLlDbKoA2B9IiNtmrJQvLoY4/R29vHBReczx0/+CE3f+xjNDc3cspJUxCeb6M5nqCvUELHiufmP8us6W/n5WUrePuM6VRX51JwZjQuFeYEJY1BxwpPuLwEAmUs8NEGDrYfoqq6igNtB3n0t4/gCcnVV1+FNpp8Pk9npyXBep6kL1/g4IEDbNq0kba2Q0gpee9738OoUaMsd0kIUIr9Bw4wfPhwuto7WbRoEZdeegl4PoHvYZTm2fkL6evrY83aNbS2jkBKyRNPPMFtt92WCnoaY3j++RfZvXs3+/fvRwjBkCFDuP/++/nCF77A2Wdbtekg8Nm1axff/c63+MY3vsmSFxbR1tbGmVNPI5FHyPg+sYrZu3s3P//FL/jcZz+L0YYH7/slo0eP5qTJJyIxIAWR8ti6bRf/+dN7+PqkkzHaMP+Zx9i1axc11VV89KMfIfAEvgfr1m/l1lv/iSuvmsupp53CAw8+zKilyznl1FN557VXE4SBS0kKlNLs2LGL+QvmY7ThkUcf4Y47/gOvuYkg8OnK97B0yVIO7D9Ay5AWLr3kEhetVEg83nzP2rdmRwt0Ganf2G2bAWMyUQFB/WYh3oT/1wNW1MZqlhkN5TjmpZUrWb58CR/40If5p//4d0aMGs2u3bs5c8bZ+L6w60vbX/PFFxdTLpc5Z84cXly8mCiKePrpp/nMZz7D2dOmUY5idu3azRNPPEl9Qz2bN2/misuv4O6772bkyJFcOfdKWkYMfeNQ2ps0LRQcwZocaEcN73E4eBpM8w3aoB0v9sfgSF1gjOmvD/IF4GljzLeEEF9w7//2rW40AVEpZ0BYITzpeXjSjdps2AWjFcVyGYHhtVdfY+XKVcRxzI7t22lrayOTzXLDDddTU11tK4mKJdoPddDY2EhDQwMjWlu58sorbAWOS8WNaB3Ou971Tpqbm+jo6ORXv3qYL37pi8yePTNNN3mywvUwLt2TgrsEKEkQRqBjBwxdiky76BDYgEwUReTzfSxbupyly5axeeNmXn11G0OGDmXPnn10dXUzecIEsjUBUaSJtWL+M/Oprq5h6mmns3f3fla9vJqpU0+lpiaLFNIKHbp9eK5PIRh8mTyUTfpZVI5YvnIVDzz0MGdNP5uuzm7mnDONOI7S1N6hg208+eSTNDQ00DpiBFprOts7mP/0fG655RaEEOQyTgm9N091dRUdhzr4h6/8A5/93Gepqa7mZ//1U86ZPYeGxnpUZNNpy5YsZdrZ0xBC0tfXx0c+8mHWrVuXNglO0oMrVixn4sQT0dqwefNmbrzxRrZs2ZKWhgOUyxEPPng/l77jHfieYPGLL3DzR2/md08+gedJLr74HWgV4UvJmtWryfg+xb4+Ojs76ero4PrrrmX7ttcYM3o0wvMoKo9H5v2OXG0TTzz9HDPePo2d+w4w6eRTqMqGlLWN+hX7Cixa9AJnTzubESNGsX7DZt73vvew/8BBbnjndWQ8j1JfkSeefAowrF27jquumkvGD7ns8svYt2cvdXX1iMAjMoaly5ezY+9uLr/scr70xS8yc/ZsGhobXLNa+JNFJBJyUr8Z6g0dP3CUBroDQZl8nQxgf9NUOoj2nycE4Et6e/OcMHkSjcOaKMQRJvCYdf45PPr447y0ZjUnT55MU10dBliw8FlGjBjB1Kmnk83muPrqawgzAeWyrbaL4hgv8GgY0sy+9jZGjhvLhMmTEaFPtraGcSdORIYBsTEERhyOZV73RI59hlokv8PXtyQaOnCeGTBH8EdKNw7aoA3aH2R/CrL5NVR6+vwXVn32LQGpgaTKpMRfegm4qjzvy+UypVKJZ59dyMrV62kZPhYZ5OjuOsji5auYe9UVTDvrDMIw5GBnN08vWMTBth72H+jkyafm88KLS6hrqOPUU99Gc3M9RthWELW1OXK5EZTLZf7j3/+Z973vfZw+derhDzhtULFKuUA23VfRn7H/pCsatPMFYFzFYX8LvZCyjKirqePdN76bUrHIkCFD2Lt3D9OnT7cAzdcUVBE8Ozp/25mnsXnTFrbs3EXshXhVdeze10ZTSxOR1QVAxYrA7wdI3OjeRqh8m5jRglhrivmY3q4SUW/Mf/3o5+x5dS+XXnoRJ0yywLF1zEje9b738NTvnqFQ1jwx7yne+54bmHzy2zjtjDOoqsqhNcRxxH0PPMi4ceM477xz+bsvfZHGhgYOtbcxfuIEwlwGI0R6Hn4mxA8C1q3bSGtrK+3tXWSzVSgFShl830oiZDM5Ar+KNa8sp739ED3deerqGhywtZd86dLFPLPwea664T08u/glWoaPYcfegyxbuQYJzJx9LrlsltVr1rPghVVUV1dz932/pr6hjqFjxjP/hRf50Z138OM77+TEiRNZt2YD27dtY9q0MzntlCn0dHcxonUYjQ31PPjgg2zbvoP3vedaCuUCF1x8EW0HO1iy+GVe2/Yq/+P9N3LenDlkPYiNhlyWJStf4bobrmPRS6vJa03JC3h1735MrorN23YwcmgTzc3NnDtrNh0dHWzb+hpjR44mF4Z4BgtYhHpTgORN/NJ4o+x+iquOuUPxOsmow+fqdJ/HtoHRNuF+8MbA1s1beeSRR/jc5z/P4qVLmXn2dIY1NDJ26BBeXbuWCSOGY3JZPE/SMmQY+/bvZ/7CF8nmsvz617/h/PPOJVddS18xtgRvran2fca3jmLDqg1MnHAiL85fyuSxJ7Nx1Ua82HDyhAnEOrZDD8uYT/lub2QDl5AmSWC+vhlxtFrfI5YajEkN2qAdJ/aHcqQM8KQQYoUQ4i/cvGHGmL0A7v/Q32fDCTg52pSAlHK5nHKozjvvfGbNmoXve4wZM5pzzpnN9OlnEQRhSkRuaKjjrLPOoLamhtGjh9PS0sill15CU2MT559/HtL38HzPEaLtuDgTZhg/fryrnEmOwUXK3HEm3B2RgqdjH/sR87Hpt9raGqZNOwOtFCNGtLJ37x4eeeRRSFKXjh9kjGH9hg0sem4Rv/rVwzz15JPU19dz5ZXvYOzYUZRKtgFshaxtr6k2xpLHtXUGSitX4m5YtXI1K1etorurmzFjxzN79izOO+9cVq9ebdODLj3pBz6bNm0ikwkQAoYOHUpzc7MlutsCKoLAZ+SIVnK5LIsXL2H48OHU1tXy7LOLmDlzJtmsbSljU46KKIrIZLO0tDTT19fHrl27CMMMcWwJ/rY6T1IqR9TV1TNs2DBKpSLbtm2juroara1Q6u7dezjUfoirrr6Gzs4uRo0cwWWXXcqBAwf4q098gs/8z08T+D5xHPPSS6uYPHkiZ589je3bdzB8eCvTzjqLG995I1MmT6G6qor2jk42btzAtddewzNPP01Ujjh0qJ1Zs2Zx4sTxXH3VVcyZPZPAD6hvsH3henr6eG7Rc9TX1VMqRWQyoU2FSpvOG946nGeeeYZZM6ezf98+1q1dwyuvrGJISzN33XUXt992O1G5TDabo76+npdffplPfOLj7jyTKKb7DRxjOtY9eNjv6A1IPRZqSaQ51mQLF96Q/yPe5DSQM2RsixWtFCdPmczNH/koe/ceonXoCFQEP/nxT3ll5Tquueo6Rg4bhRQewtj73/d9Dh7Yhy+hrqaayZMm4vsevT3dGKV5Ze0G7nngIV7duYetO3YxZMRocnXN7G/vZMS4E6htGkqkXWGCsXGginjv6z0S+01HuZ5vyCUz4ohJHDHJQZrUoA3acWJ/KJCabYw5E7gc27Dy3De7ohDiL4QQy4UQyw8ePPh77dx3jUSXL1/O7bffzn333ktNdY7amiqkgDAIyWVCjDFEUZlfP/QQP/jBDwhDj+qqLFu3bGXO7Bk0NtTbSJEy5HvzSCEcp8pGmXzfp6fnaO2EYO/evaksQhwPTEq8NYtjhRAeuVyW+fMXcP/9D3DBBec7oODkJoUlN0+ZPJmW5mZOedvJXH/9dRzYt5ennniSQqEPz/cw2H5lCWk/aSxsJQisjIEnbTVaVC4zfPhQTjv1FLKhz9gxo7j44vP53ne/zdtOtq2MjFagDdkww5lnnEH7oXauuWYuvovA2cpBgKRqT/HLX96LMZowDFm5chXr1q2jurqarq4uxy+zwLNUKtHU1ERTUyNz5sxi8uRJKBVRLBZIerUVCkW6u7upq6+mrr6WU087lTPOnEocx5RKZUDQ3NzMJe+4hDAICHyPjo52wjCkq7OLnTt3smLZshRcjh07mkK+QHUuy6iRwzlxwjhWvbwSjCHwfHJhllyYYcb0s9n26hYK+TzCKPbu3sWiZxeyZPFifvnzn/Hqli1oo1GxQkpDqVwkl8uQyQYsW7aM3z39NBjoy+cJpGDu5Zfw3ne9kzkzpzOqdThzZk6nKgyor6ninFkzqa+vT6sWlVJcfPFFDHdNaqWUTlfsjd3xm/t7c/ZmtiPRbzhZYro6bDpiGWOQAyespIEvBblsho72Npa9uIT62gZ6uwrEJc3pp0ylrrqGuGzwhYeOIS6V8SWcfdYZtB86xKWXXMzkSZOoqcrR1dmBJwXNLS0QBITVNeRq6wirQ+qaGtHSo6FlCCPHjkUZQ+D7qDhGKysTciRGOhp4PNr8NwaciQTFH+O7G7RBG7Q/vf1BQCrpAWSMOYDtAD0d2C+EaAVw/4/a/8gY80NjzDRjzLQhQ4b8XvtPxP+2bdvG9OnTueGG66mvr0u1hwqFPqI4IgwDy/Uxmg9+8P20tDSzdOkyzj77LFpHtFIulygWi2zYsJ6VL78M4Kq/LMH64MGDqb5Tf+vL5/nud7/Lvffei9Y6Fc98yyaEK8220bDx48fT3NzMnj17WL16NV1d3URRjHa6VlJI5j/zDB2dHVx73TVEUZl3vvMa9uzdxy23fJI77riDXbt2gYG1a9eycdNGW+GnrLaQUjGekMRRTOD7vPjCC3z7299m8+ZNnHLq24jKBfbt2cuUkyZRjspoFdPd3ZNG3S684AIuv+ISZsyYhtaKYrGIMTYNZ4xBqZinn57P+PHjmTFjBuvWreOZZ57hL//yL1m3bh3f/973USpGa8sVGjt2DBMnTuSUU05l8uTJZLMZtDapdpUxsH//furr6xk3bgynn34KU6ZMor6+ljiOXLVWbAUbpUepVOSlFStY88oaJ0UQ0VBXy4MPPMi6devwpC2Tj+Myzy1aiO/7DB0yhI9+9MMu8qHJVmWpqa0iVTaHAAAgAElEQVRh7NgxzJjxdpqam+nN56mrrWX06NHMmjmTlqFDOfXUU9DKsGDBAn5298/p7u4gl8swZsworrnmSubMmU2pVGLhggUU872MGTmc1iFNrF+zhldWvkwu9Bna0siEcWPY9uoWRo4c6QQkDV1d3XR0dKayDlZ+4k3WfhlhCdLHmIwR2C6Arz8Z3ng7GJECgGNNFSBRmY5cRh+5rlF4ErSK6Opo58d33slJJ02iqaGWTCh5Yt5jaKXoy/fie7bAwvcEe3fvxBeCqaedyrvfeT2XXHQBuTCgu6ODcqEPiWFocyMbN2wkl8sybGgzB/bvpaennblzL2Tz5g3s3rUD4wpBNm/ewtq161LgPuBHfJRzPsq5HHXewIk3dc0HbdAG7fiw35sjJYSoBqQxpse9vgTbKPE32D5N33L/f/3HONCjWULwfte73kVvby9f+9o3uOjiS/CEYceOnTxw33387Rc+D0rRUFfLjTdcx7x583jt1U2846LzmDJlMoVCgY3r1/PswgXs2LGND3/4wxhl9YRUrHjhhRd46KGHuOKKK47Yf66qiptuuomenh4rdeD9nuRPY1xZd4lt27axevUrCCG4/fZbmTdvHh/96Ee44PzzOeltUzjrrLOIok6GDx3KxRedRr63l4P791KVy/K5/+9TLFm6mD17diGFJN+XZ+3atXieR3NzM0OHDHVaSD5GKYIgxACzZs7gzDPO4DePPMbGTRtpamzghhuu5qor3sG3vvVtVixdzFlnTePid1wMxpANA7QxdHd18/DDD7Ft26tWc0qCMbZE/ZOfvIWhQ4fS2dlBV1cnH/vYzdTX1/Gxj32MjRs2YIyhtzfPa6+9xlVXXYUQMHXqaQwZMoRnn32W559/jk9+8hYHkhRr177C3LmXk81mGDt2NMOHD2HDhg38+tcPc8YZU/E8j3K5TBiGlEtlLr74HVTXVDPv8cdpbzvIyBGtfPObXyfwA3zfY9KkidQ1NtDT3U1TcxO+J9m+Ywe3334bw4YMobaqznLKMHR1djJs6BBOedvJdHR08thjj7Fn104mT5rI0CEtZDIhM2fOZOasWVRXVTFixHDmzXsCIWOqa6rYunUTTz/1JLOmT8evqQIks95+NufMmgHYQoOHH36YeY8/yj/907+4+0iwc+cOli9fwYwZb08r/Xzf9YczlUKMVNSxnwlTSQWny6X93ARGa7QQtp0OFVFOG1EkJfD7XiWFlEQ3++8raQOkTYxEkmi3WYFPka5n098OQDnS1dG2d6y0mRCCtWvXUi4XGdY6lPkLnqG6OuT+++/m9ttv56c/+yFXX3UV733vexg2dCiZbEBVdQ7ft9pfSsfs2r2Te++9h49/4uMYNC8tX841l7+DM8+YSk9vH08+8RQvLX6ebZvWMG7sGHa9upUdw5sZ2TqMlStXUSqVaGlpYeTIUemALcmS2mrWw58BAzXQ+henDDz3fmfqdNdEcuKQfM9yILty0AZt0P7c9nsLcgohTsBGocACsl8YY74hhGgG7gPGADuAG40x7cfa1kBBzjdjA487jmP27z9IfUND6lTnz3+G8847h4YG1+VWGPr6irS3ddLaOhxtwPcl7e0d7N27hzAMGDNmLGEYpLwipRQbN21kwgkTqK6uPmKfBw8coKmpiSAIDuefvMVzsdGmmLa2gyAEw4cNS6Niixcv4amnnqS6uorrrruWiRNPTLlZhb4C8554krlXXoHnGpqCTeUZY9i7Zy8IGNE6Ik1TGm3wU9VrQNi04ubNm+nu7eXMM88EQMcRu3fv5l//9d+YNWs21117re1R59Ysl8u8vHIVxUKBmTNnOgKuI9j3N0FaoZicrzGGYrFEPp+nqakplWgwxtDZ2cmyZcu45JJ3EMUxRhu6u7tpbm5OK8HiOCaKIhYufJZZs2ZRW1ubFiXs2rOXocOGoJVm6ZIlBJ7H7DmzHCXIfrHagE4DsnZeoVDgzjt/xPnnn8+UKVPwnGRGPp9PW5REkaJULlFTXeUqS52YaHJgBqKozIaNm9m3bx9Jo+txY0Zz4oQJCCHS6k6tdcrfW7JkCfsPHODKK+fiu+KAJFVs31fK6foHI5JWQ1LIfr8J4zhLltsWqzgVUq1EUgzafjE2koir7EwAlO8ESY3Ad5Ekk6AGKoAnuQ+ViSv3vbGtkVKNMHcPWPUSC7YAjBOsdR+nIq4J59BzYq9SyPR+LhWLFApFstVZDIaqrG0n1NPdyfp16wDDxIkTaW5qJF/UKE2qZJ6orr+04iVOOGE8jY1NqLhEVTZDFEdppfD+fftZuPA55i94lgkTJjJ37lwmT5nMwQNtFIsFRowcgecFeJ6wATNwoCpK069SyrRtkwG0UhYEGftb6P/8Gvi86A+yEgHfw39T7toK/iRNiwdt0AbtrdlxqWz+ZuxoI3GtjRu1yzQc73nCOX9jh+kG4kgc5vBT8rip9L5DipSnApYnEvRr2wCgHAepv+7Vm63mGXguylgeVAKewiC03Jtk/0KANgRBkDrgpNlrotgcx1a7yTbz1VZJ3bje9J5MNa5MwpNK9o8FXonDVcZYbpNnmwMLISmXYnK5DJC0DzFIaSiWInDb9z0/7QPY3wSHg0ub/tOHXV/P84jiMkEQOJK5d1hkQyDc8RkbUdO2bYrvB2htiCPliN0WWGiwopvSiqWGYYBIRIyMcUmkw78nm27UTkXcqbj3+3lYMr1Mj11rFyE6bDPWWSciolop/MAHbUUrjTF4fqLCbp2qUtrxx5OEVxK96A9aktQRtuJRDHC4JglcJOkzkWIm7VTlE7K6doUTykW1UpBTwVgkANFe+0oFmXXu/aMldl9amMpxmsrxW3CUkLQroEEn5HThKkg9gQuY2ftRaQJfOlmLSvG/TSHH+IGLfgl7P0sBRttqRul+72XlI9290r8gJKEE+L6PVhEZT6JNf30n4fTXAntMUhKn11kTK00YWBX79DIb3PGI9Dpq3a/li5svHOpyj5h+ANwhXyrRJ9y5GEP6m6/ci5rA8v0GgdSgDdqf2Y6LXnu/rx05qrMj6WKxSCYT4jjV1hFUBstI2R+EibRnX4XMS6qgnTz45VF0cqTnUXYNahMS8+9rCaHc932SvmnGmBScaKOd0+1HGvc8jNHY5saRHc0nEhFS4BkvfZBro9HOUdj2NsmFq/BVfM8uL7RGpQKhScosQ7kcE2YCp0mlMSgQuJ510gIg3zvMib+eJRGoVC9M2u2oWKXAK0ndqlihsd+H70GsIoxxkRoHCizAtN+r79tIht2+LXHvl58i5eOkyKECWIw24CWvNZ7nE8dWLd1PZSQqUwIeKkGpfo2oEQ402RyQ0TaFG8XaaYoZykpbYVK3vyQikwAfqFRDau2+yzQZZ+95bUwaWkxfyiQ9Z9GJRGCkgwpCOGFYW8mplUkjJ1Yiw4I835P2c2MBiyc9C4xcv0LZL81Uju15aGO/28gNYhAyZQWJfreFBoy26WAhBZGufD1CgBYSC23sGsqlGbUxCM9HOXAljcAIY/crAoSESFlVeKQk0vZaxS7CpR2QRQvKSuFJnz4jkcJzMTrX+9ELiGND4Nlq0TATVOCcFMTYwUfSSqjsWv54vofREEcxuAiwcr0mjbFNkEEQlYvuunr4vk+5XLL3sufjeRCXS8RKEYYBnudTKhYpRxFB4NPY0HhYhHfQBm3Q/rz2/2sgNRC42FGnwveTNIbpz2olcaZ2dJg4K5VKCliOg2/X1HHq0GMV90tp9TNjI0QDVdh/H0siRGlaoP+IXwqrP6NJj7sSjbC94iL3kDWYShTHHXsCSJJt2n0c/tULYaMnRoCUnnWWqozWimw266IFHsYIrDiqtpJGboQvZCLlINxhH/tBn4CEBNBoRZr20sY6nsC31xYBvvRRWqGNqoAwIVAOsCRgGGy0S0qB8EQltURlfxU7PBprt2sjC0aDFpYcXIn4CMrlsvsOKkKxsdJWcVwKpKxUb9p1ZCo3YbBRoKRXoDGaMAho7+yzbWZ8SSbwKJcjoihKI5xRFKVAO5fLUYpjCsUivm/bGEVR7Ej3EZnQ3gO9fXm00WRzIZkwoKe7B2Mit40sfYW8bWvk2/0Wy2W0Sj7PEEUR5XIRMOSyIUor8n15At8nDDybJi/0oeMIz/eoqamms6sLYxRKK+rraikU+myRh1bU1FShYk1XdzdSQK7Kit/25nuw/S49qnJZOrs6AdvYu6amir5CHyouYwzU1VXR19dHoVBAxVBVk0OrmKhcxJOQzWYATVdnB1JAtiZDsVigVCqRyQb4nk3Pdnd3obWN4mayObp7e12kU5DLhXR3ddnfjPQQQtLZ0UZDfQ7Pty17InMmkT4LISXVVVVorSkUimgNDQ31GGPo6OhECEF9fS3GQD7fh5SS4UOHEEUx7YfaMcZQXV1NbV0th9raUCqmuqaWTODR1dWJMbahehgEaZP1XDZHmMmRy2YJvUEwNWiDdjzYcZna639MyesI7Ehau35czicaQTpKFhrQyobNpeV/aG3SkLt0HJD+7t7tBWOkBQjOYSrXTsSCBC9tcGxHqzZMr6KyTRAJ27KmrGKSiITnQTmObLTI2D5vURxhtMIT4Alco1VXkeWI2rGKkziDVRyPy2ij8T2BJ2wD58CXlt/lgYljjLYK4Z6UGGFJtRZYSFuF5tIWUtrzjlWMRqNUTBB4lMslhLQAKxN4RHGMNjZN4vu2was9F0Hg2fRGuVQAY9NgYeBTKBat/IEwZDKBq5gs43mSTOgTlcsoFYOQZLIBpVIJFUcINL5vr2e5bJ1uEPr4nkeh2OfSPxCEPoW+PnsuxlBdkyOfz1MsFggDj2wuSxyXKRaL+FKQzWaI3bXT2qUKhZXBMEa7PomSKCrbcxWCILAVfzbKZfXJCsU+QIMxhO487PU0BIGX9vmz0QR7f8VxZCNs0oZ9tIoxxl1/AVonMhk2qqhUhAWUSSpIuftUp+8raackoqb7/R+0/05rHf33jBzzZeDwAYrWIKUdJCQcryR1qpUVwpXu2dP/sdufU24HLJaGYNKIrR0EJMOAJA4oASnFYGpv0Abtz2zHdURKKcW6dTdQKLxmnb9r6CuES1k4EGKrxSyYSBwWJuEIOeXxhDljtONhGEcETpIHOiUGH+ms/vxg8/8mO7oiV8WKRypNHGFdrnzBF6Bj6HMblYBW0Jc/fHk9QOJLaXABmBROR3E/PRBjj6P/mD8qVfYBR65vnPqF7xZwtDdSKozpR4VJjksdHjDtx822s97SrWdb/lrun02rJSpP/V9bxz9gmWQdbBsjgSRWmkKxRE1NrUttSxelrazbfxsCiXHrDpwvEBjR//gq++l/zEm6XQz4/PB9gcCvfC6EHVEl6wiJQbC7rYN9+/Zz0omTqMpkXbNsu4xGuPUFEs+tIzFuXaWhWC6z9bVtnDRpClVBhs6O39HV+TS+lGRcYcVhllCYPAEDeIL4bujWjwp1NEtXE260RWXhQfWoQRu049OOSyCVcEyklBQK6ygUthxzeT1AvkkNGKSnvJF+7+fNg29/G/72b+Gyy97skR3uaI5wVkc4saM5NJEuJ1KXXNmmST5LnE3qXKwzxIjDHKEU9un91a9u4ZFHDvDII29nxIgc1mEmzjFxRBVHaIRwaTq7TenZtJ09S+kqjOzxuByedTJGsHPHLhoaGqmvq7dVV86J2dGzEwwUnuMReYddH+McVhwrNm/ayvhx46ipctWQ0neg16mxi0SZvXJtjZBoI1CxZsPGzYwcMYIhLc2YfueWuMf02guBNjaKl+hSCSQ+InWeCEF7Rzf53jxjx4xGaIGQHlpIpPSQxkYLfFnhgCXOWEsvlZQ82HaI+x54mGuvvZZRra14wkYUfOEdcV984i8/zy/ufoA1G15k9OhRID1e276LqVOm8773v4f/uPPfkUK6tK89J+nOxRiB50mXhhb4fj+ZguQmx0V0jcF3ivbG8Z3SSImL6qZDB4PlDnmSXz/yOFIazrnwQnzhIbTBEwKhLZHJQEq0l8KlxDEoN1CRCKRxwNPYKkkzQLpuIBjZ/tprTJhwAh/64Af5yX/+JJ1/WORcgJEVTSrAjnmk5Q4mx9UndtDZs4OTTz4fUTaEnkD6ggsuuoCFCxcSm9gCm6ThsttYrA3FKOKllasYNsTjhBPOwBcSrYt0dT7NH9MWLFjARRdewJe//BW+8tWv/lG3PWhv3oStRFlojDn/z30s/zeYEGIBcJ4xx4/gmRDiq8BXsP2BF/wxt31cAikg5aAYAxs3wsc/DmdOO4Uvfe0WhAx4+9ln4/kem7ds56Wlq/jUJ/4XAPNfehQ8w+SJUzh08BBbtm7hzNPPZNLIqUgpeG3POrLZKtZt+iXwGcZM/BfOnv0BBjo56SqILCCwoMSYSgm1Vhrpu5GsI3N7rtrL8itkWsmjtMb3vFQQE7c8aejeOjWlFL6rWvM93zoMXangMSJdNHWa9rWhqfEjwM846aRfMHbcOBI9nyStaXS/Ci97YK6xsUstCltBppOSdANCgRagJEhPUtKKzu5ulq9+khnnXG2r5LSht6eHSCka6urwpbSTSOjQSeTPgiiNINawcdMmslX7mDRpFr4n0v5qCYBWSqX3QGLaGLQfohQcam+jp2cREyZcRi6XRSRVTv2qoKQUlMqWb2OEa5EjBY5PjohsM2Zle+CwbOlTjGw9iaa6U9IYQCwApV2QQWKiGOF74KKbeJJIuEa7wmPFyucZP+FCxo07F08IB68MgedZqJcAEGOQIgeAL5rIhsMxGMLAhtKkDMiGdY4XZ88n5WVJaQVWtWH3rt3ceccPmPf44651T4aTT34bn/vcZ5l79dX2bpaCWLnqSnehEx6YQLBg/gIuufgizjn3PJ5YMJ/A92jv7GLHjh1cd/UVhBikifvddxWqe0o9I4kk2ZSUEbZEv/9TVJgjNZBe/73bQ3qf91vSgCZyFEg3SlIWSQljKza1MezdvovJ404gwFbuCmW/52SbKe/QwIKFC7jg4gv58t9/mb//+69ApGnbe4Bzzj0XoQU/vfsubv7wP3DbbTB67JEA8K3Y0Vh6A+Pex433GbRBG7Q3tD+0RcyfxCqcAxtaOvFEaGisZ9XL6+npayXS48nWnYIOJrFjj8/zi9al6yx85hUKhTpy2TF0dkrGtJ7GK8u3UyqVmDV7Ds21Y6gOWsj4tQBkZBWhrCHj1RCKHKHM2MkPCLwAX1qZQXSMMBopbMsK3xGZy1HZcp3cCF+6RIZxlVpojS9AaIU0tk2G0bFteeFLl3rUGGX5RmiDNGCUbckiAE+4lIRWaasNKSyHxhiFQfONb36dNa+sonVEKwiRlvCnDkhUwCkGG1XQGrTGEwKUTo9dImyrDmEVov0UvCn27tlFS0sTQeBhlGLhouf41SOP8Ni8x1mzdo0DfxqMQsVWFV0kaVJjy87jqMy2V1/ltNNOsSR+o1FxGcvjIv1vszAm/e+5iEOsY1avXsNJJ08hkw3tdpVy8TyT7scCWhzHyFQ4csIQq5hyHGM8AYHH3raD9JYKnDBpIpHRaAGxUew7dJC2ng7wPYwEAun8u7FOOQkyYohjxfbt2znrjKn4nocnbashoytCC9q9FsD//tb/ZvWaNYwdOzYF3zItiCC9NzxhAYMncJ9rPM9en5tv+hC33Xoro0eP5pOf/CRXXjmXFSuWc/3113PHD37gok5OyFHaCrrYGLSr3EsiS4lpYzHJypWv0NjYSOvwVgw+sZZofLTx0HgoPPseD2N8914SGYlCooxPZHwifMr4lIRP7LiHsTDEGPsaQyzoN88BPQHKgXglBUpC7N7HEiICYuGjRADSx3g+RnjEwkN7IXvau+krFhg5eoQFuT5o3353d931X6xZuw6F24ebwA4cYgFt3d3IMEOmOgveQGBTifalcin9Xh9rsu2Ijftz95H7X/mMI9azad+B8wYpB4M2aMeDHZdACiApAwfrMObMmY5SioP795KtqUYLQW8+T1kpXnx+Eeecfz5Nzc0sfPJpxrWORkSK9r0HGTlkGM/Nnw/AxRdciGcEUpmUsCkMeLHG0+AjEJFGxBVH3D8qMlAeQWnNkuXL2fzqq8RK2zL0xBGkzzgHqg7TixGU45hYa2KtLF24nwK1kJXy/aSpcKIZZXGCK3kX2OgEMKy1lfEnTkT4PqUoItI6dZqxwVaLGW2dqNtf4mAjJxaoXDRKu+PV2P1sfe01Dh5qQwiP1159lTGjRuMJyfZt21m/fgPXXH015593Hps2b0YI4SQcSBtAa2PdRuyu3bbt28lWV1FXV48x9jpKz6pPl6PInq/Waem9VaXWKG0wQtDZ3c2hjnbGTxhPnJT3u8rLShzMahFJz0d41hMqrWwpulKsWreW//jpT+goFSliWLl5A+OmTEJWZSlJ6EPRqyLu+e3D7OnsoICmiEF5klhi/3vCggMs+CgUCwgpGTpsmNN6EpSjyOpyYUvwlYFSFFMsRzQPGcqEE09ES0GkNaUoolQu2/tL2egnplIsATjeX2RBFobrb7yB9Zs28auHH+ZrX/8GP7nrp/zy3vsA+M53vkM5VmiXhowxtmxfCEpKESlNsVy2OlZUoiI9Pb0sf+ll3j5jBgpJPpL0aZ+89uhRHkURkFceeSUpGZ8yAUUj7aQhwifCo6ighKA3hu5I0xXF9GHojmJ64pgepejRml7lJm0oOGJZpDW9WtMTxxSFoSigVyt6lKI7julSho4IikZQ0oKCgpIR9GlBjxa8ur+DuqFDkf+HvfMOj6O6+v/nzmzT7mpXvcuS3HGv2JY7xoXeIXRCDyEQktAhEHp535BAIOAQSkLoHVPcG+69yrZsqxerrcpq+879/XFHsuzYBEjyC+/z5Pjxs4/u3LltZu4595TvcVgJC/DLOEFNEkCSlp9PrwEDCBiSDiOOX6pnCxA2+92xfz9ZRYXELBY6w9Ej0j8ZhiQaVWXSxK6LxeNmsIRhRl2qHJDxmCrv7AwQDIYIhyOEwhE6AwE6A53dEZyRaBR/Z4BAMEQoFKKjo4O2tjaCwSDBYJAWXwvNLc20+FqIx+PduTP/S/+l/9J/nn6QgtSxAC0nT1PpNDauW4fFbiMClNXUUl1TTWV5OVOmTWf8hAls27SZrLRMmg41YdXtuJ0eli1bBsBJM05W/lRC7zZ1IQVLV6xk2kkn4Unykpyeyhlnncnukr1IoRGOxhG6FQONmCEJhMI88eTTjBk7jtSkZE6fOYsrL72Mt99+h7gBUUOafiAay5evwGq18uBDD7N2/UbOPOsc0tIzsVrtlFdWE0OA1c5b77zLyTNPJi09DZfbxeAhQ3j0iSfoCIeJCY0oAkPXTZ8c3fTp0YlLjZgEqVu4+uprcDlcLF2+klZ/JzEJMSn45NPPmDNzJgV5eXidLop69eKk6dN59vnniQtB1PTxiUiIIggZkpgmiEhBXAheevllBvbrx5///AqffPQxv77jbiaOHkthdg6/ue9+xo0ZQ6LLxfo1a3nhf58hLy2d5MREzjnnHMrLq0yfJogbgimTp+Cy21izbh19+vVV6Uk0nbgUxKTgqf/9HQ67iyeffoY4FiJSJyp1YijNRkzohA3J/ooKvvp8HskJCSxfvpzXXnudsWPHkJiYSGZWJtdcew0NjY2g68SkJC4lhhSg6TS1tHD/PfdyyfkXcPdtt9EnO5tZ06axfPlK0nNz6YjFCAMRBA8/+ih3X38jSxYvZuGSJcw66SSSvV5Sk5M5/Ywz2b57L8FYnEA4SiQaY0fJHtIysqisquHRB37DxDFjyU1LIyc1nRP6D+Tmm39GaUUl7aEwHeEIV111FS67nS07dtEaCNLS0UkkqhhkLG7ga20nGI7R1u6nta2d5tY2QuEonR2dtDQ309TUxMWXXIY3JYUGXysNPh91zT4GjVDI9I0NDbT7O6lvbKGq/hBV9YeobWyiwddKRX09NY2N1DU20RFU3v3hcJhde0tZs2kzMSCGRnX9Icpq6qhubKG0tpHKphYWfL2WK6+8igG9i/A4E8jOSOP8s85i3pfzqTnUTFllPb62TsqrGtlf0cj2feU8/uijjB8+lEyXi4LUZCadOIZ7f/1rdhw4yKLVq1mybh2l1VXsr6kFoLq+nvKmBtbt3snCtWv4eutWfnbTT0ixWTn15JOZt2glB6tq2XOwim2797Fj914OVtRwoLKO7XsOsmFbCfsOHuSrJYtZv20r20tLOVhfx/rduxg9Yhgei86K9Ruob/Uxe+YMTp8xA4CnH3mEdLuVs6ZNonhwf37z8COs2bKVurr67r3I19JCScke9u0r5dNPP+WUWer7Sk50k5eVxcRx4/jlbbdRWlpKeUU5ZWVlHDx4kP3799Pc0kxDYwMHy8spq6igqUVFTfh8PubP/4ozTplDTlYm2ZlZzJk1m4ULF9LW1obP56OlpYW2tjZCoRDxWJwXXngBYKAQol0IERBCbBFC3CyOSi0ghCgUQkghxGtCiP5CiHeEEA1CCEMIMa1HvdlCiC+EEE1CiLAQ4oAQ4mkhRNL33Mq/FQkhyoUQ5ce59qA59mlHlUshxDIhRJoQYq4Qos4c8y4hxI+P05ZNCHG/Oa+wEKJMCPGIEML+DWOzCCFuEkKs/Vet8zH6uNG879fHuZ4lhIgKIXYcVe4VQjwuhNgrhAgJIXxCiPlCiJOP0cZVZh9XHacPafo19SzrXnshxCVCiHVCCP/xntVx2rWba1zW4516QAhhO079GUKIr4QQLeac9gkhnhBCeI9Tv58Q4i9CiBohREQIUWv+3e87jLGX+d5EhBCXfdv7etIP0keqJ2p5l0w1ZYoSpNatXsVVt/4CfzBEXUMje3aqd2va9Om0t3fwxbx57CvdT7s/QF5REa3BABs3bsSblMSA4SMJCmUiiZiC1KdfzOPzzz5l1uw5XHe9Sqr75VdfsnHTRjZu205qSgpGVDnfNjU1cdqc2Yd0ZgYAACAASURBVGzbupURI0fyo8svp8PfyYY1q7nmisvZuWMH9/76AexWKzIOgaDSLqxes4ann3qK8cXFXHLFFTQ1NRHTNJr8fu645Rbe/usb5OTmctqZZ+L2eNm8YQMPPfgAS5Ys5p2PPlVYVYbEZbOYvkPKBKNblDYpbkgiMcWAN2zZQlJ6Br3yBW+/8Qa/uuVnZGRmMmPmLFLT0mhtbWXHtm289uqrnHXBj3A4rCA1ItEoUkqFMRQIEovFiMUjhExMpBXLlvHQffcxZPgIrvjxj1m3ajXvv/culZVV3PPAg1x/xRUMGz6C8y64iLKyA3w+73P27NnLvC+/IjklGX9nJ2ecex4b1q/ns08+oXjKdPZXVKELidftwu/v5MUX/ojVZqN4xkxqm1upqqkh0NmJYRgMGjSI2tpa2sNhNm3ahGkD5De/foDtmzcx59TTGDPmRDZu3MDrr73GokWLeOPd97FabQiLjt1mo7m5iSsvupD6ujoGDR3GWRdcRHVNNds3bWTd16toaWzmpNNOJ9HpJBwIsWtXCQDv/e1Ntq5ZQ/GUqZxx1jmUlx1kwZdfsnHdet75+GN0u4NwNMryr9fga23jg1f/RNOhOnoVFjFp+gw6O/34Ozp447XXGTtlGq7kVPbu2cOeUhVEMX/5SoxlK3Em2MlPTwWgo7OTqtp6UpKTCXYGCIXC6JogOSkJZARfayuxuMSTlIKwWukMhAhFIjgSnLzw7O8BGDt+AnsPlLN+00ZGjx2L0+1i1do1VNfWoOk6+VlZTJ0wkYipbalvaGD+kqWU7N3DrNlzMITBZwuW0u5rIzHJy4QJ49iyZSsP3HQ9QX8HBf0GMGzsBBxWK0sWzGfJgvk8//IrTJsxC5sm8Lhs+MNhbr/2cjatXU1yegannn8BGvDlJx/z4hOPU1NewZiTZtC/f38WLV1O74JeAMSlxJngpLauHn+Hnw/+/DIrFiygaMgQ8keMZOXXX2ORMWZOmoAlHkMXEjSdkCFIkgafLzhEXrqXQKfS+kycMIFgKETJnn3YbIpv+jsDdHQG6D9sGHa7gxXz5zNu8hT6Dx7MgYPlZGVmUNSnL4HOANnZWd37U3JKCrn5A1m8cCEXnX8eHo+H0884g+ycXHy+FvaUlPDh++/xu2efVT57SrGIJpSmDSAnOxshBA31SkCrqqzk6ssv58Tx47nmmmuprqriww8/5JIfXcyGTRvp17dfN9huLBbjzDPPZOGCBaDc/d4AQsB04DlgHHD5MbbWPsA6YB/wNyABaAcwmfhvgBZgHirZ/DDgV8CpQogJUsr2b9q3/wOUBKwCIsD7gAM4H3hFCGFIKV/vqijU6fxd4CzgAPAHwAZcDQw9VuNCCCvwGTAb2Au8yT+5zsehN4AngWuFEI9KKY9WN16N4tUv9Rhb19wHARuA3wFpwIXAAiHET6SUL/GvoV8CM1FrsRQ4plBzHHoXGIt6PlHU+j8IjBFCnCl7OMEKIW4A/gh0Au+h3sFpwJ3AGUKIiVLK1h71xwKLgERUjt/dwEDgUuAsIcQMKeU3pkwRQgwHvjDbOFVKueg7zK2bfpCCVE/qWubklCQyMjMpP7CfQw0N+CNRonGDXds243S58KSkk5yRDcAbb75FwQmDqPW1sn3zBuLxOKPGT2DRhg3EYzFSk7wcam8D4LNPPub2hx5j1PgJJLrdTA6HSUzP4P2/vs5zv3+WK6+5FpvNhtfr4Sc33si2rVv5+S9v5yc3/4xVm7cQisW5/Cc/4/F77uB3//M01gQPU6ZMo3/vvtTVNwGwdPFiHnj0Mc675GJC4TCBUIg1W7bw6YfvM++tdxg7cSI33PIrItEYGRkZXH79zbzwzNO8+erLPPbY48w58xyampqYMm4Uwc5At9kt0ZNIu78TKSQdfj8AZRVVzPtyEW63m7+8+CJWq41b7/0NHX4/KSkp3HLmaRwsr2LxokW8/ubbBIMhJk4sJjk5GatFUFPXwPZt28jMzGTYyKGU19aoOSxcyDW3/pzZs+YwYvAQQv5OrrvmSlavXsVlF57PVVdfz6/uuBOA9PRUbrj2Gt575y2WLV/BGWefhWa1cda55/HkI4+wZuVy7M4ErLqGjKnn+PwLL1BVUcHwsePYW1ZNYb+B1DX6KCsrIyUlhbbAVkaPGka0qYmGllZ8rWpf2rx+HR9/tYhJE4sR0iAejXHXXXfypxdf4M9zX+SZ51/odvC+9Sc3cKi+nkd/+wyBqMH5555PeWUlH33yKYs+fpc3X3qR2aeexfhRY1i8aBl9C/uwCNiyejX3Pf6/nHf2OfQvykfGJffdew+//e1TrFvxNT+943Za2tp496N57Fq/iqZDdYwqnsIfX3+dVatWM3vGdKICFq9YSdGgQXz2xZc0NzeTka3eV38kwqTJk8nPzmT9iuUAeL1e+g8YgMXMyydEV2idRDei5GTnIYVGXEDEAEMTBMMRnnzscV5+9hkysrL5459f5UBlDW5vMn0HDGD5qlWsWLOOWbNnMnjIELZs3EhGThZr1qwGICs7i5NnzyQiDU6aMY2S0v14kr386LyzWbtxIx2d7bzy2ycJ+juYdf75PP4/v8WmW7AAdeX7mDltBnf+/BZKDxzA7Uogye3kiScfZ9Pa1RT068/Vt93O9ddexcGySooGD+OtF57ls7ff5OLLL2fM8OE01NZj19WWlJ2RgSvBSeX+Mj76y6vs2LSJ2eedjycrh4svuogTBp5AsKMdt1UjIcGKBUlMChKEhVpfOy6bxpmzZ+Fxufjos3nEI1F2bNvB4IED8SS6AUhyJ3Fg3wFuuPkWDlVWsmL+fE6cOJH8AYMZM+kkZs+cwfKVaxjUvz8DZ0zlpOmVVFU+ZJqvdV595c8YhsHCJUsZNny4aYZVGvWW5qbD0hOmz1tXsIGALm+3rpimr774gj++/DJXXv1jhJRYgLkvzeWnN93EH557judfeEE52UuDxx59jIULFvDTn/6U559/fpeU8kbVh9CBucDVQoj3pZRHJ4yfBDwupbynZ6EQYjpKiFqDYiY9mdVVwKvm9dv+4Yb9/5eGA38GbugSPoQQzwDbUcz39R51L0Yx8bWoqK2QWf8BlCByLLoXJUT9Afh5jz6+1zofj6SUfiHEX4GfAqegBFnMvgRwLRAA/trjtidRQtRc4MYugUQI8SSwEXhWCDFfSln+bcbwD+gkYIKUcsv3uPcEYLCU0meO716UMHY6cBnmnIQQBcCzgB84UUq5p6sBIcQLwE+Ap4DrzTIB/AXwAJdJKf/Wo/5FwNvAG0KIQVJ2AdEcSabm7gOU4DZZSrnte8wP+IGa9rqop0YqOcVL8eSpSCkp2bqJSCjAhHFjKNtTwpjxE+gI+JlzxqmkZWSwZcM6KiorCIaDbDSZxKhx46hvqMeWYKe+qbn7FD5g+AjGTJ6M3ZnArn178QcDzDjjLAD27S2hV24OeVnpxMMBvvjsU0aOGs3P77ibfWVV1NcfYvqkCYwePpTLr7kOKSUVB/aRkZ5KemYSubmZAAwdNpxf3P4r8vPy6NunD7puYeeuEratXY+u65x09rmkZWaSk5+Hbrdhd9npN2oMHq+XVcsXo1t1klNTyEjPoG/vIvr2LqRf7yIyUtMo6pWPYUhCZqqaKdOmcf11PyY3Pw+ESlHiTvJy2VWXkVeQj8Vmp729nb37S0lKSWbKtCmUlR1k2JCBBAJBGurqOP3UUxk5fDgOp4N9paUAnHPhhQwZMZKhQwaTnZ1J3769ueaa6wDIyMzk4YcfIi83h4JeOSQkOLjuenWtprKC1ORk0pJTSE9JZdLU6bQ0NbF22RJSPF6y09M4WHqAbSYg64OPPILTZqWzvYOWhgYuu+g8Tps5nXg4THKil9IDB/F4PfTt2xuA8ZOnMnHSJJwOK3arDU+ii0cfexSv18v7b7+NVdNwOOzsKylh1YoVTJo2nXqfj4KCXvQpyGXCmJFMKy7mkcefIBqJUF9agttiZfaUKfQvKgDgzHPP5bZbf0pBfia6EFg1jZtuvAGAzZs2IjRBSekBkpM9bFy1kkRvEuf/+Bo2btmCI8FORXUVq9esY+rkSWRlZ+Ow2znztFNIMZNp9y7qzQn9+rBz12565eUCiv9adZXnTpcGmoxjESrIwGqxAWaOOiGw6AIhDeb+4Tmee/QhktPSmLdsKd7UVLZs3croUSNxJThI9ngozM1j1tRpuO0JpCR60dApL6sAwKJb2LJlCyOGD0NKyfLlyxBINm7cRDgQJNPjZc/WrXiSk3nm2Wfpk59LUU4mhTmZTCwez0UXXUhLSwuffPhhd7TmX195FSEEl91wI7Nnn4wRN9i1s4Qxo0/k7IuVFv2zd98h1OnH7UygrVWZunTdwua1a3n+kYco2baN515+hZlzTuOCM85k8ujR5Hid9M/PwmnTsGhgxGMg40gjQsXBvQzuX0hakhchJe4EB/72doxolMEDDmv8nXYroQ4/hVlZxIJBVRiPU7prF61NjWzdvJWhA/uQnZFOOBbD6BEIIDgcTeuwWhAyhiYkFk0iZJy01BR0DYQ0evxXASe6lFikgVVKLGYbxROLueaqq9ANia7y+HD11ddgsVjYuGFjj2hJwR9feIGsrCyeeeaZI/ZLk9H/ElX70mNsqYdQAtHRdIv5e11PIcps8zVg63Ha+09TAPhFTw2OlHI3SlNzghAisUfdLnPfPV1ClFm/BXj46IZNs93NQD1w21F9fN91/ib6o/l7w1Hls4Ai4B0pZZs5NitKCPEDd/fU6kgpS1ECiQ244juO4Xg093sKUQAPdwlRAOba323+eXWPepehxvyHnkKUSfei4Acv72GGLUZpn9b0FKLMPt4BvgYGoITavyPThPcFUAOM/2eEKPiBaqSUAHWkn5RmtTJt9kw+fv9dVi9dwhnnnENjxUFaW1oYPX48rYEgAwcPYtLUqSz88kuuuPEnzJpxEk/c8UsA8nsVMnrwIHy+VnSXk84OpcEZXzyRKcXjqCivoKPVx6hhg9lpmnQ62ttx23SEjLNs3VoztYrg3rvupqWtg7TkRNpqK5FSsm/vPgDafc2c0L8Ii8WCrqv3+8RxJ2K36AroTwqy0zPISM2gurwcT1IS29esJ+Zrobb+EBarhWgsxqGGBhxOJ6V79nCosYGJ48dh0zWsmlCYOWY0VkcoQkNtPV3m+gED+pHiTUQTcabPmcMf//d/eOiXt7Bw6jQGDxvGyEEDEUaMAf37ctYZp9DU1Exzgwd/ezs7t27jzNNPIyU5GZ+vhade/ANJXsXsh4wYgUWAJzFROboDWTk5AAweMgi3OwFdVxGAUhoUmAJBXU01FiToGls2beL6665nwRef8ZeX53LBuefS2RFgx7btbFi7hoEnnMDIoUPYsH4jFXt2ceLQE0h2OggGQ9iNKOE2H+vWrGHylClsqVbMf8r06Vg0BVohTcfzFLeLEcOHs3zFCvbt2snwUSPZuHYNALFYlD1bNiOCAR7ZsY3WtlZafa0kmVqK0pISEgQkOO04Heqb9SQnEQp2kuhygaYQqnsV5APQ2uojHI2we88uslK9GIZBbkEB06YWs2LFCnr1yicpNYXZA/rj9Xjw+Xy4LBrjhg/jY3MdO31NrFy2nFEjhmGXJnKolCpC04z+1IWmkvMCXdhYXfF2OlBVXcUT999HosfD6x+8R0Hv3ixZugyLXSc3NxuMODXl5UydMI70RA+7Nm1hUFFv2lt8YB4qgsEAleVlzD55BpVVFZQd3E9qSgo5uYNJTkpmy9pVAEwoLqYwIxNdYkYUQhwr02eczJtvvsXW7du5WGi0dbRz8MB+MrKy6N2/Hzm5OWzdtZvtu3fy40svpnRPHgA7tm5h05bN9O1fxIrly9Rz2FPCJWecTiwa5Z158xgzdjzvvPU2Y4cPx221YJcR4oaBFIK4piGtCcpBPdjJnpLdFBdPxBDQ0NKiMK00gW6izYdMoUloBnk5WditNqqrqgHwNbcwcMRInAkOiseNwu5wUFNfhyYk1eXlODTM4AG49JKL+fijj5g8eTIXXng+06ZNZ+KkieTl5pkRs/wdToJuevTLuMrGoMXVMxwzchQW8/2VQiARWCxWMjMz8fl83VrJPXv20tzcTL9+/XjkkUcAcoTCx+lJQZQm4GjaJqUMH6N8AsrscoEQ4oJjXLcB6UKIVCll8zGu/6eo9DjmxirzN4nD+L+jUDvn18eov+wYZf2BVKAUuO9ofmTSd13n45KUcpcQYgVwihAiX0rZNYfrzd8Xe1QfCDiBVaYgeDQtAe4DRn6XMXwDrf8n7l1+jLKVKMSYnuMbZf4uObqylNInhNgCTEHNfds31e9RPsnsY8VR125FaSdXAWf2FPS+L/0gBaljk6B4yjQASrZvo6hXPq/PnQtAr959SUlOxuGwM3ZCMR+/9x6p3kT8He1UlpWRkZlFKB6jvLKK/n37UFjQi21rlaZqyJDBCClZt2Ejie5ESvaUMugE9W3EYjEMEz+opU0d1DZt3Mimjcc3uza3tKBbLGi6QhACyMrKOoy3IyTNzS247A6klLT5fHz54Tt8+Q0zDwUCJHvcKDghFa3XFbPudDqYWDyehAQHAA67ncbmFto72jnt/POoOVTHro2b+OLDD5j3/ns89cCvKerbl1vuupuU5CRWr13LgAH9qKyqwpvkISUtBQEker0M6N+fFrc61EUiEZI8HpzOBEwjBRarAgMdOnSYwsWSh7GaujLVR6NRInGDqoZDNHS0ccqcOcyYNYvFCxawY98ewpEI23ZuJRwOc+nVV7N2+1YyeuWyZ08JI4pPJCzgYH0NzvRkdpaVommC/fv2Ulmp9plRo0cRlwaBcByb1aKS0wpBZpbyafG1tmFIQYvp1Ltm5UoAFs7r1p4fQe0BPxFdaRKwqDkkJnrYtmMnxePHY5jmGd2qPp1YPE5dbR2RUIgkU/DKL+hFfnYWVl1n9LBhuDweqiqrcdgdNDY2kpqcjNvlJm76tQ0aeAIjR42ktqaG1g617yssJI2m5iaSEhOx2y0msqxUcBtaVy5FlR9x3Zo1hMNhJkydwqATTmDz9m0s/no5UydNRtgstAcDVDXUMnH6FNqCfg5Ul3PihBNpbWvrhmJva2/H5XGj2a2EomESPC4mT55I2d4ydmzfhb+pDoA+ffvS9RJ2HYbjQIa55q2tbegCOtoUj0tJSSU7LZ09+/ezcPlyevUuwJ2SBDYroJziI/EYBQUFdHQqHK3SvXvwt3eQV1hIVkEvqg/V4klJwpPqJS4EMWHtjjJUgCACQ4dQJEDYsJCSlkVDcxsbNm9n2LAReD2J+IMxtu7cSyCoFBKhqKS5rZOS0jIampWpv6W1nVNOOZV9+8tYu3ErVouF5ORkmlsaqTlYztC+KgI2FJfMOuNsPvj4Y5793e949dXXmTv3ZQBGjhrFgw89wkknzzBxu0S3IGTC0hKLK5gTExwftzeJsKHSXVmtGkY82p2YPBaPEw6HEUKjzjS1l5aW8tBDDwFko0AGjyb3Mcrqj1EGSmCwHKedo9v8IQlSrccp78pjoPco8wItsivh5JF0rHVJNX/78c3r8l3W+R/RCyhh4VrgASFEFnAmsFVK2VOY6fJRqjtOO13l/6ogge87H1DauSNIShkXQjQDGT2Kv+uc/pk1mILawBb/K4Qo+D8kSEkgJzuX1PQMGuvraTrUwNfLluJ0uXA4PfQtKkITgqL+AwHYunETBw4cQErJmOJi+hT1YULxeKwWC7GYQXOz2g9sNhvxmMKIGtCvN72LCrGafhpdp8OYEcNjamZuvvXn3PjLOykpPciMqRMQhiQSUcli62vr2bVrN+2dQTyJrsPwDeLwRirjBhXl5Yw/UQnUeQVFrNq2A6uQrFy9mqnTp/Lhp/MY0L8fvYuK+OTjT7DomgKDNKWxLmdzw/S7iMZihEKmtloabNq6mbz8PELBIFddfx3jXn4Zf1s7Sxcu5KtPPuWT99/jwdt/xayZJ9NwqJ5JE8ZTU1OH2+NGImltbSMhwUEsElECBdDZ0UF2VhaH6utxO10ke73dTLS8olxBMUqVy043/XpACX4xGWfdti2kZmWwc18Jo6dMYtH8+dx3992c/qMLWTx/Pg6Hg4GjRxKTBknpqURKDDS7jcraWnbt28PYsWP5av58Lr3wAlasWElqSjIAu3fvZsSYMSR7EqmtOURudhYGgrpD6vtNSk5GCiUYAlx140+46PIr0Ow2cgpy2bJ1C+np6SS5PRT1KsBmsRA2JKWlpUTMEHOhadgdCUggGlcJ1aLxw/g/27ZvZ8TwYbQ1NADQdOgQnZ2dZGaks2TZMoQQ5Ofm0a93IbU11fTpXWTm+lP7fFNDPWvXrMbtdpPm7dofBFEDNmzagtvlYsL4cci4yt9mxGNE4zF0iwWha2i6TocpgCEEq1avoikYYOiIYTS2NrNt9w6cThdOjxu73UZHoJOIEWP3vj0KxqKqEgCPx4PFqvPRJx9xwuBB2CwWXvrTXEK+EJdefBGba1W9uto6WlpaMKIRZFzxLKfXw959SitrsVjoaG0nGlCH8saGBras3YCerPy+IsEgnYEggU6lFUbTGD5iGKFopNtE3WfwYMaOH8fLz/yeS04/g18//TS5+bk0+3zIWByiBjabHd1iMWFEJEnJHtr8IXRLAqvXbyHQGSA3Lw+3J5mW9na8ySnsO1CG3a4OHekZmbS2drB56w5yc5V2zOl0EcdKfkERu3buJCsrg7SMLIKRKH379gNWEo5Eqao5hEXXGT1hMn+ZMImmxkZ2l+xi2eLFvPHaq5x/7tl88PkXDB48BF3XCEeiKmmy3QFIQiGFP9fQqmSBjkCAikOqTafdRjTUSSwWV1h6AmrratGEIGa+k2effTYffvghmqZ9l1x7xwOeagM0KWXKt2znX00GSut1LPpXCQNtQIoQwnoMYSrrOPUBPpJSnvsd+/q+AF8fogSPa4QQD3EMJ/OjxnascYMSrnvWg8NJOf+O54t/HJX5zwCWZQKVR/WnowTVntrEnnPadYx2jp7T91mDLroGuAslrOpSyvuPO/pvSf9nBCkL0ObvZNjI0Sxd8CUrFi1m9YoVDBw8lEH9++F1JRA3JEK3kJ6RyeqVK/AmqX1h/MRJxA2DQGcnwUAQd4Kd5sZGADQpiUejeJxO8rKyIG5QV69OfVJKYkYci6YzauwYNE1j1dcrOfuSy8jLzeLAgXIcdicdHR2c0L83tgQH0VgMoetK0DHHLlG4U7rQaG5qBiPOgAEDKOrTl6qKCqIBP3HNgr/DT2NDE/FoFMOQrFmzlmgkSk5hBuFQlASHIGhG10mhYbFaCEYihOMxQgFlrljx9SryC3ox6ISBlOzdS1NTEzVVVezYsZP+QwZjd7loa29l6fwFLJw/HykEHR1+3G4X+w8exJAST2Ii/fr2pam+ntpyZUKLRiKsWb2aooJCpk6ZTFurj05TeyCEoK29TSUwtlmxWa3dzu+xWIyamhrWr11DUlIyGRkZjJtYTFpGButWrmDMhHFUV1RQPH0ae/bsYebMmViFAMPgww8+oLm5mREjRqAj0KTE43KSnZHGmkVKI7Xwqy85+4Lz2blrJ6vXrOaSiy8mFgixbcsW7A4HNqeLyupaCvso35hd27ezbdtOMnMyyS/MIxoMsXvbdgYPHkwwLY1Dbe2Eg0F2797Ntm3KbN7a2obdbqeu/pBKAh2LE48qASIUCiOkxGl34M7vhaZpHNi7l727SxjYtx9VVWqceTnZVFdWkpuZRSQapqq8nIC5Rr7mZhLdbtJTUlQwAeD3+6mqrqamtpai3r2prK0jEgrjdrsgFiUYCmGx6Og2K1abjbT0dG7+xc/JyMklweFgRO/ehCJRwqEQGckprF61mvyMLAJ+P16PhxGDhhD2B8jJzGRKcTHvvvwyNquV2dNm4HI6kQIuP+9CorEYyZ4U0pOTcJlauI3r1qJpOmg6ErBadGw2O+vXrgVg5IhRJDgcZKSlUVRURHl5OZNOHEvewH58PH8BI4YMJcXpIsk8sEyePIXctAykgLNPOYVn772fPoVF/ObhR8lKy+CRe+/lqXvv5cN5n+NNcIGU2DTdTBistGEAmi7Iz0zjzNmziERjuN0OHA4HsViMpMwUCrJSEcDrzz4NQG56MnkZ45BGnLWrlYa6IC+bwpw0JDCoT173/pObPoTaihwaqpVvVU5eFpoQ6ChFYVpyEif078cF55xDblYmDz34ILu2bGbm9OnmXqK+E8NQ6OpGokTXBfvNA4En0U1hfq7CcJMGFt0LyG7NbmHvQkCSlZtFUlIS69at68ag+hfQWuA0IcRgKeWxmNi/m3zAsOMIOf+qhMybgZNR5p6lR12bdoz6e1Aar/HHGde/nKSUUSHEyyifoDNQmik/KvKvJ+1F+YeNEEIkH0OrMt383dyjrKtO/jG6/ncmvZ7KkU7yAJNRLL2n39UW4FzUszgiD5Mp6I1ARUyW9KgPx352Pcs3H+NaKyoK8XOU2dYupbzjm6fxzfSDFKS6tBlCiO6ovUBnkIDfzrTpM1i64Ev+8Nv/wdfSwhVTp1OYn0dnR4i6hkM4bFZOnFDM0kULSEpSm9SYE8dhAKtWfk1CQgJ9C3spfxeg1ecj6PfjdbtYu3o1CQkJ3f1Ho1Fq6+ux6joul4uzzz2XD99/n+d/+zTTTzmNosK+pKWlU11dRV11BVWVFSR5vbQ0NxN0OGgzT5ttbe00NTYpgaO1nZzMLNpb27j0iit55IH7uf7KK/if3z9H/759OFRfR1ZGGjXVlVg1nbqqCgb360fDoXrwJCrNk6ZyxCW4nATDEWrr67o1El53Iv369CEcDHGoopL0rCz2lZRQmJ9PZmoagY4OwqZpw5mQgMPloqammqFDhnDyjJMQQEpqKhLJxRdeyFemCaygVy9mz55FeloaiS4nnZ2d6GY4dq/8Xths6kCpaSo3nK4dT4H6CAAAIABJREFUjmPITk/nthtvwmKxYLfbcTgc/OSnP+XhBx7g1eeeB+CGG25k0tQpOOwOhCY4e/Yc/H4/DocDl9OJ3e7g4rPOQaBx2syZbDUZ37qVK6mtrEQIwejRo0lPS+c3d99Ne3s7l1x+Bdk52WiaxrTp05gwaRLrVq/mR5dcwmkzT8Zq0Tjr5FkKUVzTKNm5i/T0dAoLCumbm0dTpTpITRw/nhMG9DPnpmHEFSI8gN1h58zTTkVoKg/e+RddxLtvvcWn77zNM3/4AwP79lHpXTSN9rY2SFV+Zpqm4fV4AJgzcyaFRUWA4GBZGQCJiYndz2LkyBG43W4QAqt2OENjV/QmwNRp0+nXvz/epCQys7IQQkNIFRWmAfnJaTjsNmxW9Zxyxo9HmLaxpmp1cEiw2hhcUKi+QaB3RqbSeJolI0cN56STZ7Jk0UL+8sqfuO2Xv0RIpXFdtX4dH73/PsnJyVx66aXYbTbsVhtXX30N999/H08/+Th/euNN3MJC/9xcjEAnLzzzWwBuvOFGUh1OJJKAefhx2Wyk2hO4/867yfQmccvPbuacU+bwxRcLKOiVByIGUoG9dsEZhMIR7BYNT3qSuSrqGRkWvRvpH8Bi+rt4rMJEu7eSn5EGwKGaKrz2rvyQKkpOQRhIbObCa1KSoEkWL1pEcXExCQkJytRq4pM3NShtaKIzAauMKvT+rpRXmvpGDKHatZlj1DDQjQhWTeGuSVQuzK7sBIaUqr7NwU0338RjjzzGLbfcAkeDrgNCiGwg2XS8/jb0DHAa8CchxPlSytqj2nMBQ6WUa79NY0LlrUN++zxr61E+Lz9GRaF1tXMVMPFbtvGP6FWUIPWoEOKkHlF7KSh/oiNIShkTQjwH3I+KgPuFlDLYs873WOdvQ3NR2pI/ALkoR+8j8rxLKSNCiL8B1wEPAT/rMaY+qOCBKEcKMBtRH8QlQognpZQBs34KKhru30X3CyHm9YjacwCPm9de7VHvDeDXwM+EEK9LKXsm2H0YFZ33cg/fs1UogXKS+c6+31VZCHE+yny3j2P7xCGl7BBCzEHBJtxuClO39qxjPl8vUNfl6H88+kEKUl3UIxgBq0UnPyeDiy68gAfvuZ09JerdveC8c0nxKj+e/KwM8rLTqZw9i88/+YhAZyf9+venePyJCvnaUIjlVl0wZJDyg0pO8pCemsy0SRNpaGwkGo2Slami7axWK7nZuUQjYSwWC8+98DzVVVV89M47bF63jomTp5KVnUVNTTV7S0rYunkzr/71DQryctEE3VFZHk8iOdlZSCAjPUNtirrGvffeQ0NtDXNfepGTJ01gxqxZ5Obn0+pTYf9rVq5k4rRp3HXHHbgTErAiMWIGmlUnElOevs64QXVtDTnZ2WwHxo4eRe+CAuKGwcSbbsLucDChuJiCwkIMCatXfc3mDRsYOWo0F5x3PhaLReUCtOikeDxH6HAtqWlsSFJa39SkJAYNHNjtO+tw2JV2BIVgnpSc3J36RAMSTSHBZrPhdbnxOo90Jbjpuut58tFHqa+rY+jQoVx24YV/rz9OSz/iT4/djoESDlKS1drOPuUUzpkzh9EnjmP4iOH8/tHHWPP11xQUFvLEU4/hcdlBKJT61954hdNmnsovb/4pr839IyeOHU1Skofq6lp27tzNrl0lLF/2GTmpo0A38LhM853RgmbUgiGJSaObYaprQYxIqZmHUfLEEzeya8dG/vzSS6xYOp+TZozDarVQWVHLkiXreOPNx5g4aTggiUaUGT/YsYrO1v1IKen0KR4WCVejs4phg8LI6BLafWYaEjN3H93JRAyQknffWc6vfv5nzruwmP/93dUIw1CCEiZCP3FC5hi7MMyljIOUtDSqPWv37k1cdtGYro8PunuQ5OZ4uOW2qdxzTx92bFvN3XfcwbxPnmfI0Ezqatv56qtSNE3y8CMTaGi4h4ZDqt8zzozy0UeZzPvsEyaNymHY8ESWfupi4YIKmltCXH31ILJTX+TAnj8CBtVVime0+Zayb/d5gGTKpDi/eXAoD/5mO9OmncDcF0eTlaNSA3UJR7oOu3ae1D03dc3oXh+pEgsCBn6/mu/2zaNUUhapMgikp+u88/ZfaW/7mMxMHZDMmeMkO0sJYfG4skToQiLiMe64/Q7KKyqYNnUKBYWF2KwWNm/ZwpIlyygo6MWPLjgbjbj6IqTslnhkPGYeNASaMA+N0kA3xwoahuxy7+nKzahyVYLg7vt/zfbtO3jppZcAhgoh/oKKPspA+fRMRGk1vhWDl1IuFkLchWJwpUKIL4AylP9PAUqr8DXwD9O7i8Ne2d8Fev05lBD1RyHEDJSz+HBUZNY8VKj8P0tvARehfI52CiE+Aawo3KkNKOyno+lhcxw3onCMlvBPrPO3ISllpRDic3Oc8PdmvS66C6XZudnEU1rKYRypROBmKWVZj3brTOHrcmCr2YcHOBXljP2vckw/mkqAXUKInjhSfVDaoG5BT0pZLoT4OfA8sFkI8S7QiHr3JqA0hHf2qC+FEFcCC4F3zOe5BxWpdzYqyOCK40EfmG0EhBCnoyAQbjGFvG4oCdT3cCXq3Xztmyb5gxWkunyKuj5LI3YAYY2RmS4ZNLgvu3aWkpqaxIABccKBDSjGEEPKOONPPBz1OnHiAMIdS5FGHMOIIzQII4mElAY7FNhOsP1L4vEYdj1OglXgb1d7QDzaQEvj28TjMZXs1ojxtzev4q03i/jk4/V89vE7hMMx0tISKSxM5/5fn8vI4VU01DyLlHFamhR0gL99DdUVT9HlGiulycQE3HVnFmPG/Ig339zI4gWf0tERwuNNICcnkYsvG8SkSYLG6ntp0TBz66k24iZTNYw4/pYqdE35p9SU3YmIuJEY3HLrAFavqmPThgXM/zKEzaaTnePk57cN4aILs6ko/RGHo3q7dBsmczKZbk2limaqrnqSnVtfV8xJGoBk/z6lLT5U+zLbNn2hmJV5b22tOjg0NX3Chg2Du4UAkN31xo+3sGJFhFmzqli7Jv+I9eGout1tm9erK1T7M6Z/weATDN5/fxkb1iwjIQHmzIFrry3nwJ48DhwVSPv738GHH8KKFTv52992YhiQkgIFBfCLX0Bn4AzWrlN1Dx5UvxUHbmfzutuP+Z76OzawbeOQI8qefgo++ACWLi3ntVfK0TTIyIDZs8CI/pydW1U9M9Kf0j1X0mG6zZr4jHS0LqGuQgWkNB/PnbIHNZp1/G2rKdu7+h/f0IO6QLubmvx88MGxo5z79IHzz/saXcDzz8Mbb8C6dRWsW1eB0wljx8Jll8HAgV9Qd9R4H38c3n0XFi9u4/N5bei6au8nN8GMGbtpaD7Mh3zmOoTDlTS3HHatmDwF7rkHHn88wFVXr+S3v4WcHNhvnlunT4f29mMePv+OurK9dAaOtGI99BDMnQuLF3cQCCiZZtCgNlKO8hySaBhC58677ubjjz9m8+ZNLFq8BE3T6NWrF3fddSe33nYryWnpxKWBjEt03aI8HKX6PlTiRkGXP7REA2FVApfQOVqZYxiiO2DFqtt47/2PePetv3HllVeGUIKGG8V4ylBalKPNQd9IUsonhRCrUNqMSSiG14YSHOaiwCi/DQ0zf9/+Dn3vNjF9HkOZtGKoyK4JKHPPPy1ImYz3ApQAchUK2qAOpRV5CGU2OvqeqBDibFRo/lX8C9b5W9IrKEFqo5TyWKYppJQtQogJKCiBc4FfoKII1wNPSykXHOO261A+WBejMKsqUVAJT6MEsH8HXYhap0uBHNT79CDwRE/YBgAp5QtCiP0oENjzUJGJVeb4HjsGNMc6U4i8D6VtPANoQgnND0sp9/6jwUkpQ0KIc1Dv6/WAXQhx9TcJYMcicdRc/iM0ZswYubFHJFzPMa1f359gcP+xbvsv/R8mw1CM1+eD998H09L6rem11+D11+GZZ2DEiGPVUPoxIboYlmZCamhHlB95/RhlKqHhkeXCLDczFnfFYnWXC+3vyxCH++9xT882kUf2rcxBAqHpZp2uco2u9NhdbR05N9E9vq5yZeo7PBdNaGbKIfOeHuMF0ITqU0qhTMlS5TPU0EFoalw95msYEiGUWVcaqqwzGEK3WhV2WWeAkt17GTF0KK4EJ0iBLjQzRyJIoVJOa0LHgtK+KGOZ1j13YfargkCUXu7luZ/ym/vmsnjlXAYO7GMGdqi6mjBzU5rz7Vo3dV0oPy+EkmvMeWhdQoxU0XkWXUUHxg0VoahpNix2L7GoQ5kF0bBaNcKRGELGkFJisehEY3F07TCMi8rdCUKCw2YhEIiAAF3XsOjqfiOuDjMOh4VILE4sFkfXdGx2i8o0EIsj0EhIsOOw23Do4rs6m//bSQhxCwple+h/yN/q/zyZcBYPANdKKf/8Hx7Of+lb0D/USAkhXkFJ4g1SyiFmWQrwDlAIlAMX9rCB3o3yio8Dt0gp5/8zA3Q4+qDwx1Tm9iM3/C7GozZ/gW5qsI6+diRTFUKnJ9PoZqJSILTDTOrvmSwcZohmeReTM9X3WvffPfo3N2fRjf/z9wzbANra/GgWC263B19bK/V1DfTvPwCrbkETuqmhUwxHCo24ISkrq8Dr9ZKZloGQh5mDZjJOITSkoSLPpNCQQqChI2RXMmYV9SWFWhchFHMQQqBJc12FcioWJjMSqHYEXQyti6GqtjUOj7OLYXehOgtU+Ucfzqeu7lauufYSpp70mPID6RIAjhBE1PpoZp+6CaK1eMlTwJMMGfIVY8cUowkLFt1CJBLD6gBhkaovMwFz1z+jK8eiORcpjW5jl6ZpxOJdGjmwWHS0WBRDGuqJmn5fXQmtNV2Zf2JxJQzouvKTisfj3c7FQhPKp0rEAQNdtxA30+4IM9ly3IibuR8FFt1CNBY1kQ4kmtWCEIJIPIYUSuQxNAtGPN4tMKlk2jE1QyHQEYTj0e4oT13XMWIxYrEYVosFTdOJhEIEImGkRcNhsyOlJBKJEI1EcDldGGbIvc1qVaCmdjsdfj/hcBi7TUW9BYMh4jEDTWikZ6Ti8/lo7+jEkJCalsbB0t34Q0GS0lLZd/AgLmcfGoO9aY9a0RGkp6bQ1NRILBbD7XEjdEHAH0DEldCSmJhIMBwmFAwSjcXIys4iHovja28jJnQSHA6+/rqSCVOnornHc6jNi81mxedrQxqSRJcTt8tJY1ML8bgyX6emptDa2k4oHMJmtZKamkrDoSYikQhCCHr1yqa93U97RweGIUlJTkBKaGhuJBaLk5aejtMpqa0rw2q14XS6cSd6aGxoJBYLYLHoJCcn09DQgMWi43S6sNlsNDc3o+s6LruD9LRkamsOIYSGxWLB403E19JKOBJG13RSU7xEQgHi8TgOhwOPx0V7u59oNIbFakHTNBIcNo7hHvVDoKnAp/8Vor4fmSCiN6JS9bz1Hx7Of+lb0rcx7b2Gcnz7S4+yu1AYDE+YtvW7gDuFEIOAHwGDUWq8RUKI/vLvcwd9IwkhurVSw4Z9iZSSIEKF+0uOxrgDs8ww1IlP07riNbv8WLqYljDrmFAEJpNVzsAmUrSmfBIsYJ6yhXkSVY7vcZOB6bpGXMaQmmK6mmbBMIxugauLkYLE6HJ9ENI8MfesJ4lLA8OQxK0N7N27j9R4Kjt37mLq1CmkpqaaJ1FIcNgJhULdp9yamhrqGnbSu18xdqcT3WLBIjTisRiGIXEmODCkxO/vRJqYP4nuRJp9LcqAJ8HrSSQYChGJRJCAy+kkHI4Qi0WxSkGC3YHdYcPvD5jroKFpEAyG1dg1sNqsdAaD3ebY1JQU2tvbaW/vwG6z4nI5iUcjBINB/vynFwkFQ7z91pskOBM496IrqWswsFptNPlaQQpsNit2m41AMEQkEkXXNTyJiQSDYWJxFdHY2qHyGO4tq8GedMB04Ba0tbUjBDjsNpwuJy0treiahsVqxe1yUV9fj9VqRbfqJLictLa1qrWPGWRnZ3PoUAPRqFq/3kVFtLd00tzcjMViISkpCYGgtdVHKBQiPSODeDxOu7+VWDxGWloqNquVBhN6welMwOt209DYgNDsGIZGr175lJdXoGkaTqeL1NQU6uvr6QwEcCTYyMlLo6XFR8AEjczKzCRuGLT6fEgkXm8SaDoNjY047HYsFgtej4fGJsWoHQ47CQkJ+HyNCoBS10lLTaOxqUlpQDQNj8dLe3sboWAIXdPxehSURTgcIhKNYnjV/AOBAFarFafTSSwO7R0BDCkxiGGz2YjFBYZUybRjMQNdt2KzO7HYrAjdwuBBg1i9Zg07Nm5m8uTJ9MrLIRxRJlm7zYbDbkcjDSGECWCrgyeth8ZI6/7W1Teno2mCvOzcbhytj+d91g1UqgmBEBKyUgADDSWE9kpP6jaLCSGQ6d4j/u6Vltgd4K1pGjLFjSSzu1+AgQXpap8xcaH6ZrpRhyEl3PZNyzEHqu4ZmJ1kyjmqjsxNMQ+C6u+cZBVcYFaBLG+3Y3m3m5E4LCplpx6d3qzHxR8QSSnP+0+P4f8iCSFOQzncn4GCDPhVl0P4f+mHT/9QkJJSrhBCFB5VfBaHwwtfRyHD3mmWv2161peZ9s4TUTmcvjX1NO0pbKIoh5p9BCMq9N/r8dAZCBCJKGbqdruQElp97QgETqcdSYxQKEg8HsfrTUKg09ERIB6L401MQNd0Wlp82O02bDYbFqsFv19FotltNlwJTny+VlOLBMnJSQQCQQKBAJqm4Xa76ej0EYtHiccNkpKSCIcjhMMRQMNunvJD4TCxaIzERDdSGvg7O5FS4nI60TRBIKQEEKfbg9vtwqLr1NfVkZ2ZQSQUYv++UkBiSElaSjJ+f4fpHGvQ0NBAakoSLc1NtDRL3G43Mm4Q6Ayg6RoupwtNE4TDESLRKCCx2Sw0NzeaGiiBzaowiMKRMHa7A4fdSiwWIdDZiV2zkOCwK02LpgRVu129MvF4DMN0Urfa7Rgy3q3tEQIcDhua5jEj7mxEI+H/x955x1lRnf//fabctr2xFZYOAoKAFOlNUNTYjWKLirGkqIkxiRFjCbF8Y6LB2GPvRoNYaCKIXUGkdxDYZenbd++9M3PO748zc3cXjSbfFv3+9tnXvO7O3DMzZ8qd85nn+Tyfh4yMDGbdfQ+2bdOrdy+unzGDfv36EY1EsSybcCTqe4IsP9yhPTOmYWBZJlKidXSUzjgEKCnuQLcu5di2jQDycnOwLZ11ZVkmedm5GP6ga1kmeTnZeoA2BMIUFHbIxzQ04T5sh8jNydGeNgWxaIiivByczsVYlvATFgCKU8KjQggcV3u1TNPANARdykv9MU6f386dO4EwcKUkErYpLslOhaAsS1Bapon6UikMy6CoqAjLEHhSErItpAJUue8V1EC/Rxdd3BehM7rKO5YQZLkahkB1LEEhsQ0Tx/MoKizU3DzANExkSQm2YaI8ryVL1jD8frf+/ZECB4UFeQgh8DwNqKVUvuCkfoHJzclOAR8ts6X43glTkUoDZSHADEJdSqGkJD12WExXpTCOv622aEGQopOnsETrfguUdjSrVuHKf9n+mXXMw+YPW8f8+vngPLRdvWWZOPz7dvu/bmeiic170STnP31983b7Ntk/xZHygdTrrUJ7NUqp7FbfVyulcoQQ9wIfKaWe9pf/FZjbOjXxq+yrOFKBdwO0FlFdPIFUCk8pwraN67lBBroeRAWs/Dj6rxz7d9JGjPZ0BpGUCASmYej/hX6bFv7o4nnSH5xlaoAFhZJKCzm2aqPAByoqNS9EKkDqh5lkKqSpvX8+ad73zsngeinwpOeDjiCnqmXA1Rlkft6bbOHzKQWWZbcM6j54DO4BpbRnz7JS7kZcv2RPIHwaWLD94Nb2nY4oJTENE09K7bUUWiXcMkJaLVyY+vj9kVxJhWV6qdRzHeILQrL63OrOWQQp6hB4Rmizf6lcTEPgeA6mYfrtDKSfeWYKE1dKDMNOARQZZHoJkVomaKVPlnKoKlxP6gLH6L62zhKTrf5PDdj+MsMQrTekT23rjqdgSwDSAjCJHzLV3wYBbalaQA6A42kVdsMwUp7gNhddBJDTXwS+R4o2y1ut1ApotVpX6XtGaz5oQAXmV7iv/2+ZEOJbxZFqt3b7/9H+u7P2vuqp9ZVITQjxQ/w6Qp06dfrKjQVgyrIsMmOGJr1KpbVdjJCuP+a3M/6PPzADM/wwpGWauK4LniJkmhrYeNInBoOWeBKYwkJK5ad5g2Vb+nxJQCks29I1BKWm9QaDlB44JcFCHdLQXoQATBmmqffp6dCK8NtKqRC+/o72gihQ/vZEAHJUKnxj+GFWwx/INWjysEwjNe4nnSSWaenjFwZKKUKmD/DavAyIVoDJxTQt/wDwQYZEiCDkK1FKYijXX8cnTSsQaHCoKyRqwJkKy/r9N32wJjB88jT6+KTU4C4F4MDwC9eG/ELE+iy7PpjQIMA2BAqJIUBJD8swUkKuUvgAVOq6e/j9kcH6QmEJhePqY9a/Cb2+bRi4rqtDZ61AkkAX2A1C2Jr4LVt+sIEEgvI0t0vq2n+mMPE8ryUTDV002DANTOF7m/QlJ2wZfhjZwwy0rVr3wf/Xx/kIoc/3Vz81aAWMRKqNaNVY/KP12q3d2q3d/ofsPwuk9gohin1timJgn7+8grbKqWXA7i+tDSilHsIXXzv66KO/9PhrzZMCXQ7M8/Tg6ldNoEWOUAVsAwDGjPFojjfz2WcrOProwX5ZBlJKwMGbrB44NEHccZK4rkskEkFKiTQC4jPU1dWTnp7mk70Fge6MbVi6bIPrYZoGTtIlFLJJJDQfKJFIUlRUgFRual0pg/2LlAdID7h6oHNdF8sK+FaauAzwwQfa2xYIXSqlCPkeHOl5WJaVAi0pUrT/Zq5BVZAF1fI2b/gDmSkCroZfysY/pyrYl9CEYk2yaXudDNOAwPPkX66QpevCqcDjYJj+uCdS1ynwVbmuhzKEn+2laetKCPYf2Mf69esYOnQosViEkO911Dvx9DUXWina9K+jPgQDx/PAPz7Pddi3b5+udwh4PqCQUpO/LRFo+Qjqa6pZt249Rw8ZimHoArdK6kQD5UkMw0RJiScl8XiCcDisAZNyddJBQN73SXqebPGqWaaNk2jG9gUxA76ZCLxcyvckKidFQBdKAxOUTIGz4LoppfDclutumoYf1tLHowWyNSjUMvuSJUuXMnLEMUQiUd8b6HvDfMCm/HvOcZI0NDRoTpgQGkT53j8/MKe5QQKk59EcTxCLRTRA9gvvGn4Isrr6IDt37aJn716ErbBOGzBahHZbzoXAkwrDv8YB0A6uqybUe375IZBKoKSbOicE3q7Wrq3/P96t2q3d2u3fbMY3N/lKm4OO5+J/vtpq+dlCiLAQogtatOxfrhwdgI7gIRlMlmW2IZ0Gg0rqYepbMpkk3pzgySee4vXX3mDy5CkUFxeTkZFBeXk5xx9/PG++OZc1a9aluFazZ88hPT0dy7IIhUJELJuwZRE2LQpycvjeCSfieZI9e/ayYcMm5s1bQDye0EBAKerrGrjpplv4+JPl1NU18PDDj/HXR5/k9TfmUltby+9m/h7TtLFtm4UL3yKRcNi4YRMbNmzh8cee4umnnyEej/Pmm3MZP34Cubn5pKdnMnTocJ566ulW58bHJoZIfVq2lfrfMAwcJ8mMGTfQo1s3crNzmDRhIp+vWKHbiOD86vZvvbUQ07J4c+6bLdv1lZgFUFGxixk33MCggQNJT0sjLzeXMaNHM2fOqy3hF0P4+xapfglDYJgGhmn4ffYzCf3tSin1wOgD1mQyifQUmzZt4z/uvIsHHniYysoqDS78AT7pOC0hKX87pmm0eLZ8crIGpB5CGCQSSZ5//gXmzptHMpmkrr4ex9EFYaWUJJMO0lO4rsumTVuYN28eTY2NVFdX4zgujgNOApoaHTas347rGKAsXn99AU888QLJhPQdN3p70vPYv/8AX+zYyaatW1m3YSNvLniLp55+gXizi+sKkgkFygRlYYoQBjYCCwjKnmgvbHBMhmH42XaG73lt8dQGvxfD/40E60Pg+dEh1Orqap54/An27dtPTU0NTU3NmmTv6QK6UsKzzz7P5s1bqa6p4ze/mcGhQzU4rocnNeDyPElTUzObN2+hoUHXgquo3M111/2S+vp6DdwME8d18fzzW9fQwONPPAFKUFFRyaJFi2hq0txFzw8Pe1KSTLo0x5uoqtrDpk2b+HzlSrZt2566TolEggMHDrJ6zRpee+01Zsy4kcefeJKmpiaSTlJ7wdoApxSTqt3ard3a7X/U/hn5g+fQxPJ8IUQFWt/iduBFIcQlaFGvMwGUUmuFViRdhxZV+9G/mrH3NT35p1vu2LGLlStXs3XLNs565EzS0zOYOnUqnTuXs//AflatXMXCRW9RU1PHzN/PJC8vzPIVWojwyCOP5IQTT/IBgKCxsYm5c+fRu09fKioqefbZ5xh41EAWL17C+HHjCZkGruOyYfMWdlRU8Nnnn1NdW49h29imTVZuPqvXrmPm72aSlpZGY2Mj8+a/RUZ2Lg89+BjTzvk+a9dvoV/fbjz00F+55pqryc3NpX//ARQVFTF//jymT7+Us86CK66Af2Zw+NWvfsXdf7qb008/jdKyMp5+6mkmjJ/A+g3rKC4uTjF2GxoauPSHP+S8887lhBOmfnnbAi684EI++OADpkyZzJTjprC7spKXXvobp55yKvf+5V6u1J36l6+fgSZT44cMTdMinpDEHcnipe9z5JF9ycjKoaEpjuNJotEICFNPh/VTHbZtJQxWr1sLaI9e775HUldXx8LF7/Daq68y9YQTOPGEqSB19pnjuOzdu4/nX3iRxsZG7r33fmpqarjiih9R2rETQkDccXnl1dc5//xppKVF6d1nAO8seYe77r6P0SOOYtSoEQhh4EmYu2AhO3dV4CmFYViMHjOKe++5lxHDhtG1S4b2lEk/SnVYSPrwY/n1zcNYAAAgAElEQVRqM76iVdsl0vecaZBfx/vvf0AymeTFl/7Gzh07OPmUUxg9ZjSeAluY7N2/j/lvLWbE6LGsWrOODsVlGHaY6po6MrJihEMR4s1xmuMJbr3tdm6cMYNOael8smw5PY/og+cpko6HZQmEYeFKRUNjI81xh7q6RlauWkNzYxMbt2zhiH79KCjIJxFPMG/evFQB8RUrVtCpUydCIZum5mZ69uhBYUmpLj9kGuyo2M3rr7/O3r17OfXU0+nbpzdWKIQwDf8+anVn/EOOVbu1W7u123+v/TNZe+f8g68m/oP2M4GZ/5VOfeV2/4VH4n0PPEQ0EmHxkrfo1asPP//Frzn77DMwTO3VaoonmTPnNQ4ePMSWbV9Q39jE2vVaAjvpepz+/XPo07sXSimeeOIpioo7MnDgUUTCUXKy8+jYqZy83ALWrF1L9x49CIVsDlQf4qfXXE1FRSUbt2ymrLwzuTnZ9O7Tk/GjRzFo8GBMy+KD996jYncVa9ZuxpWCvftrKSrpxKCjj+aYYUPIzc1l3oK3WLlyFSeddCJXX3UNH330Hi++uJ0xY2DsuK8HUkopHnrwIS666Ac8+ugjAJx26smMGzeRp596iuuu+4V+Vxdw3a9+RTwe5+57/oT6B+SSM848naefeZKSkpLUsu+ffRYnnXgyd95xJ1deefk/Ae1Em6un0F5D0zRT4RrpeSQdyfYdlSQSLv2PGszBmlruve9BMjMzuOCCc8nKytThQWG02dbhwKqpOcF99z/EpGMn0bt3b3r36Utubh6gcFyP7Nw8kq4kZNm4nmR31V6ef+EFtn+xix//+Mfs3bufqVMnE4mkgSHxpCISC3PxpRcSjUSZv2Ahe6qqWPLuYs4440x6HdEbhNYUk8pj4KBBfFG5h5qaOuoaGhmc9OjSrSsFHfLxlEqFIzFaQpwtx/PN93kQoj18aZs50y+JpBRz5y/g2Wef5dIfXsqB/Qf4+S+uJS8vX4temhB3HB58+K8MHTaczOwc/v7q6wwePJinn32eV199lT/84Tb69OkLhsmqNevp268/uyqqSDqS196Yx+Rjj+W2O/7A9753IsOGD8VxJJZt8e4HH7KropKa+gY2btnK7Jdf5pFHHiYjIwPX81ACCkuK6XNkP/Ly8tj6xXZOPf10OnbshE5Q0GFVV4LruHTt3oOkKzlu6olkZGezduMGtr75BhMnjKdzebkO/wZnot0Z1W7t1m7/S/afDe19q831JPfffy+WZTNm/FRKyzpTXVMLvkjkBx9+zCfLPiMtI4N7Zt3Hpi3b+cFFl/jrehw4VI1hmCxb9jnbtn3Bzp0VVFXtpbk5SXZ2Dp3Ly+nWrSsLFi4i7iQJRaIMGTaE2voGFi95hy1bt1NYVMCIUSP43c0388X27dz/8F/J9guyZmXn0qdff/I7FKOEzdnTzuDmm24mkUhw+RVXcuSAAUw7bxofffIpY8aN5Ze/+jUAc+bogffr/vbv30dTUxNDhx5NEN4YOnQIADt27Egte2fpUh64/wFm/eXP5OTm/MPtXXnl5ZSUFNMSKlGMGHEMAPv37//G/qh/EGIJwlKgPWMvvPASz784m+efe5GJk48jKyePhCMZP3Ei555/HplZ2ZqzJQJhUT0hRJt5JQw8Jenbvz99j+zHi397mbyCfLbv2MFjTz7F+EnHMnT4MEzLwpUShaCopBSFwTU/+xn9BwxgyZJ32Le/huZEHGWAoxzmL1rAtdddQ03DIY44sidNyXqy89IZNXY4GZmZCGHiSTAtm2haBq4rycrJJSMrhwWLljJu0ngiaSEMS2LagOmhRNtJConE+MYpyGtsO7U1BQjTBMMgJy+Xk087lWMnH8vGLZv5YudOGpoaNdtMSt7/8FMisQyUMKjau48TTjqJouJijhk5kl/88pd07d4DR0o2bdnC3175O0f07cese+9j/cYtXHrZZYwaO4ac3Dz2HTiEKwWGZaGEYNy4cUyePJns7GyiaekMHHI07330IXWNDWAY2JEwQ4cNo1v37mRkZhKKRljy7lKWLF1KwkmihIFpW+w9sJ+Zd9zB4qVLSbguyjCYPedVyrt2YdOWzby9eLGfxKCPO6jH2O6Oard2a7f/DfvuACl1+KRT+V1HoTw9BZZMNlNXW8OU444nHDGYNesP3HP3n3jw/r/w0Ucf0bFzF1wJufkd6Nd/AEcPOZoFi3Rds2QiydIli5n5u5m8+frrHD1oMLU1jVRW7mPvnkMUdihl9apNNDd5dOpYTn52LkLpTKbaQ4c4uG8fQrnUHKrmpeef5S+z/sxvb7mFPn16p4r8Tpwwls8/+5gRwwezccMqXp8zh1UrdWjxuClTMJXCFgY1Bw5w7tnfZ8rkyQCsWAFCGV87FeQXEo1GWb5sBSgDlMGyT3W5pvJOnUEZNDcl+OH0yzjt9NM4/fQz0LfBYZO/Pb5ieuJxzdmaMmUySmllc3XY+qLVpEc3x59cUBKkTz1XBpkZmZxxxhmMGTuEAzVVYDhIkhw6tJ8NG9aTnZPjE5hNXM8hmYzjuQ7Sc3CSSYSUkHQQjovwJGnRCJf/8CJyszNobKhm/rw3mPvmq/Tp1Y2QqRDKxTDAtG0cKVm4aBHrN6ynY5dynnvpRVavX82zLz7Hnn17MRDYhsnwIUMZM3oseTm5dCopw4kn2bR+IxvXbyRk2+DXIDRQFHfI54TjJhMxBR1yMjl2/ChOmDIZ09c7kp4WWE0mkjhJRxPYPQ+kxDx8UqrthELgout/tkxKHTYBCMHOXRU88cTTDB48hPkL3mb5shX8ffZrLFu+EqkU8XiSog4dOOXk7xEOhcnLzWPwwKN4661F5Gbn0L9fH2zLwhSCaCRKVmY6kYhNz97d6de3F0uXLka6ScIRm/T0GCBpbmzgoQcf5KEHH+SNOXPYXVlJWjjEyKHDuO9P91D5xU4MT2JJCAsDL5HAlIqsaBpHdO3Gio8+YseWrUQtE+FJQoZJeiydsaPHErKjFHYoome3HpSXdiI/O5+unbv5PDeJlALXFXjKRCqd1ei4rpZOkRJPSS2dEsx7uj6n5zooJZHSRXp6UkrieQ5KBvNem0/puf59qJcp6aXWU9LD8xykdHHdJFK2aut/19LG0/uQwbY9pKs/PcfFc1yUJ5GHTd+G8l7t1m7t9i0uWny4HR7MCOYMQ5NgE4mWmpPLl30KQFZ2Fk899hC1tTXMe+PV1PdDhx/DiSefQVFBLtJJkJudydlnnc4j9/6RXTu2c9stv22zr44dO3PqqSeQmRVhzpy/c/bZZ5GRmc7KlZ/huS579+5l69ZtxJubSU9PIxaNULFrJ7+/9UaOGTGCU087lcaGRkK2Pt2NDXUc2L+Pk048kRWfLWPcuFH89oZaAKLRmE5/V4pJEyeQlhYjFo0SicD+/dDUFCcWi/3j8yTg0kt/yKxZf6a2ro6SklKefvopMjIymHbueSgMbrjhRg4dOsRf/nKfBjr/gj322GNce+21lJeXM2vWX2gpz/N1DgBPhw5bZ2qhsCwLz/VwHI9Dh6p5/sUXuPzyS9iydRuWZTB6zEjWrlmbkmOQUrFy5UoWLVzIVT/9KeGQlsBAgWFaICXCUwihNZUEkB6LMfTooznphBMQQrB582a6dO7ip+ebLPvsM7Zu305xWRnRWIz+/Y9kx86R/PjHV2ieketiGCZp0Rjxpjg1h2pYu3YdxwwfzsnfO5mnnnyKfr170iE/H2FYSE8fV0N9PWvWrCYSjdKlSzlOMoFrhlOJE5s3b2X2q7O5+KKLyc7OAgTCaps00eqq0vbMtfXxiS+10a12VVSxaPFSSjuWY1phOnYqZ8iwY5g27Ww6duyI4zhEo2G69+jK1q1bQQgef/Qxpp13LiWlJdTV13HbHfcxbdqZDB06jLT0NDoUFdKnTx/mL5hPbkEuZ599JtFYlAMH9hMO2QgUGelpnHfOOUSiEZxkkqrKCoYNGcKSt98mGolSUlSIbfrCElIRtkN4rodQ0KtnT0YecwyWbenwr21jmjZZmVm4nodl21i2TV5uLonmZrZt3cZJJ56EwMQQgsZG/RyIxsJ4nqPL/Jh+FqQvs2EaJq6nr6tSCum2ypBVWmcNPwTri0QQqI570sMI7nnRomGmpM4gbnv9gixdXUIoyKb1PE+XbPLbBEKpgd5boMPluZpeapi6rfRJ/4YIJE4OFwVtt3Zrt3+HfWeAlK5V1mYkxpNa3FAJF9drTn2VkZEBwPPPPkNZx45MmXoC3zv5FJSSPPHXR3jrrYXsrdpN1PQoK8jGdJN0Kirg/PPOY+/evTz++OMI22bVylXcesvNvPfuu1x5+cUseucdMrMjVOz+gtVrljF23Bi/fEo98UQzw48ZSlp6lIpdlbyzeCGNjY387rbbuP+++zjjzO+niMVr1q7j6quvJj8/h0gkzKpVa6it1UBq1Zo19Ot/JJ999hkPP/wwM2fOJD8/n/R0iMehpq6GaNrXC4/efucdRGNRnn/uOQ4dOsTAgQO58w9/oKSslI8+/ph77rmbJ558kvz8fG6++WYefOAB9u/fz4ABA7jnz39m5MiRfgZUW/D6+OOPM336JXTp0oWFixZRUlbKP5Nlrgf+IDVdAAbCEngKkp6ktraOd957l2nfP4sdO3aQbG5GOg4H9x+gsb4eS+gMfiGgpKQjDU0JGpuTGGZYD8hC+OKiBqapFa2l5xGLZZBIuFRV7cPzYM+ePTz88MNcd9119OjRnX37D5JsTnDOWWdz++23s371Gmpqa6nYUcF/3HEX8XicKVOm0Kt3T1577Q3CkQhzXn8TpRR5uXlU1zQwcswYVq1ZR2GHArr36MkXO3ZRU1tD3HHodUQfXM+jvr6R2++8i4b6Gq684gp69OhJbkEhyrCpqNpDTn4BUkpcJbAOExc9PCqqz6V5OLb6kjUnEmzZvp0TTprKnr1VrFm3irz8fGobannqmafxpEeP7j04++yziEZjVFbtJuHEKetcRjzZxK7KXeTkZXPV1T9lz549eFIRjaWRSLrMX/AWsWgaabE0IpEo6zdsYsfOXeQWFIDPaYplZIJSeLhk5+YRS0ujsTnBaWeeSWZOLi6thFQNk3g8wY6KSoRlIiyTuONg2TYKQXpmOsqA9z/8EMs22bhxI3urKlm1ZjW791RRVt4RV3k0NsSZN28B0WiUSZMmEA2HcJXEFPoeaQGgCgxTi6cIwy8d1aLWLn3ldgW+or9KxQqV0GHLQFgWXydMCA3KhALpBTIUWnAVX2DU8IVVPWVgKAMD4Yu6+hnI0ApUKbBaJEs8obMSlVZFPYx12G7t1m7/TvvOACmVYj+0WqY8Ai3utPQWL01zUwuoeuFvL1PQoQN3330PlZW7ue+hh5gwZhQ7duxg4MABHNW/P6GQSWFBLh1LiujRrQs52RlIy2L02JHMfu1VRh0zgg3rN/DmG69x8sknYNkWffv0Ii+/AOkp+vTtS78j+6GUIicnh9/OmMHfX3mZY6ccz0sv/Y3S0lI6l5enxBCHDR1CSUkxnlRMm/Z93n//g5Sbfuz4cWAIuvfsyRU//hGZWVl4qkWlWwrBN6VBWuEwt952G7fedlubx208mWT6xRdz/NSpTDv3XO65+25uvukmbvztbxkxciS3zZzJ8ccdx6YtWygs7KC5Jv66lZWVXHn5FRQUFPD20qWUlpYiaSkJTau2h4/rSghkyvMlQAmUL2ugDDBDFpMmH0t6Wjrbtm0nJyeH+vp63nvvPXr16pUCUUrpcNyU46cSS89AmAae0KVrEAZCKJ/3o1BKEI3GOO2003nxhZdoaGzEsiyOPLI/2dk5gCAjI5Mxo0dRtWc/th0mLT2D4pISZv7uVsKRKKGQRSgcAgzOP/9cwpEoyWSC/fv2s2fvPj755BM+/vAjMjLSOPPMMyjplGTFqlWAoEfPXpxy6kkYpolhmAwbNhikR2FREVIp7FCYYcOOobxzF5rjScLhEEoYOF86d4cv8HP2Wp3kFD5tZaFQmBEjhmsJCCdJaVkZxcVF/Pzn1xCORAmHbAzDJBqLIYCCDgUUFBbSq2cPPl+5kj1792BZFl26dqVLt66gFBHT4vLLL0MBTiKBRCAVvLN0KbEMfe6Snta7MvElMCyTsvLOKMPirLPP1iExFbxTaG+yIQwUiqKSEn2fmKZWQTe12j6GwXHHT8EwDI4ZPoRwOEI0GmHDhg30O7If4UgE0zQgaZCVk004HEYCnpK+PpV+bkhfiyvuFwfWoqVmSo3O8zykBNPUZZUC76FpakFTmdQAq6V8kRaHNUxTh5iF/p1GImHijUkMAUnXJRqJEI8nsHzPkm3bJJqaUn0KhUIpb1gi6RCLhVO6dgqI+tp2iWQCFERjUWLRGKZo90i1W7t9G+yfKhHzP21fVSIGDhfl/HJ+kwyUORFI6fHBe1p485ln4JFHoLQUnn6aL9l//Ae8+Sb86Edwxhnf3L833oA//AFGj4Zbbvn6tnV1cMEF0LUr3HVX2+z222+H+fP1tgYPbrveKadAbS3Mng1Zh9cnBY4/Xnukdu//jGg0TKA6HdDPW+bxUUfAoQgCE4rf3XIff334b7z/0TOUlnSgzxEn0bNnZ/4++25AsWvXHgYOOIuf/fx8fn39Rf4Ao7c959UlTL/4d0y/9GR+f9uVqQCTH9hI7T9YJ6VonoLAvlho0M7nkQhDoKSHIQwMdAkglFb3PnjoECHbIpYW9UMvUrf3j8l1XUxLC1Emkw6NjY2kp6Vh24YOFQGedAmKRweDdyB2KoTAc108z6WhoZ7snGytvh4EXYQuA6OVurUIpekLw6aqqqDarKM9GB6eJzFN7YEoLPsVtpWG9JTWuPJcDf8VCMPENCCZdLFsC8PyvSL+9bP8MFMgzGoYBqYwcBw3pc8VCLnqPgtf0FNzgRRQtbuKDoUdNJAwTSKRCIlEEum5JJNJwpEInuv52l6uX7RZV9rWxYEN0tMzqKurIx5vJhaL6cLHtbUkkgkaGhrJiMV0IWPXw3EcsrIyicXSqKiowHVdotEIRUXF7N27J1VUu6SkmH379um+ui6xWBpSujQ1NyKEID8vn6STpLGpmeamJh1aLChk565dKOkSCYfJzy+gsrIy9ZwoLS2lpqaGRCKBUJLc3BwaGhpwHIdEIkFpSSn1DfVa90opigqLaWhoJh5PoJSkQ4cOSKnYvXs3hiFIT88gJyebiopKpJRkZWWRkZHOnj17aWxsJCcnm7y8fA4cOEBjYyO2bVFSUsLevfvwXBfLtiktLaGycrevcWbRoUMBVVV79C9HKfLz82hu1tpa8XicDoUFNDbWkUgkUt5Px3Woq6sDID8/n4L8AqKWjWEY7SVi2q3d/s32nfBISSmRCDxPYPovYUIIpBKsX3MStXVLU94egI6+tnp6+ldvz4/84dc8/kYLgE08/vXtAPbt04BoxQqYMOGr21x7rf5sDeQ6dtTrVVR8GUgdPKj3XVAAG9cM+uc6fZht3gx/vhuuuQYO7D2ZHdtgTxWMHXOAVZ+PSrXLyoJPPn6S1SufbLv+Rv0Zb36Vtatf5dtqNQf+8+s21P/39aO1HaiegutpZXw9yO7FNLWafteu3Thw4AAHDx4kGotRVFxIXX0D9fX1NDc306lTRzxPcvDgQQwhyMzKIiMWY/fu3di2jWmaFBTkc+hQNa6rdbGys7NoisdJJBJ4nkd5p07s3bePuH8D5+bmID1JXX09yWSSgvx8XM+jublZAzPDICMjg0PV1ZimqbW2EnGSiQRO0iFpJpBO0idV6yLimRkZuI6LEElCoRC2ZWOZBgUF+QBEI2Fs2yIvL08rsRsmsViUouISXwjWwLZtpPRw/XqEpmX6wqu+8JkvPJqddYT2aqHFY3Nzc/wQswaR+fn5KDQ5Xxd5buHnGYZBsSpqeVkzjDYq6wHHqay0qEWLSgg6dMgPWiAEFBV1SIFpIQSFhfmBlBVCCIqLClL/CyEoyM9ps48OBblt5lXbTqBUhy/1qbW1h/bard2+PfadAFIAlZUHSCRdlFREoxE8z6O+vp66ugNIr6FN2wEDwDQ1KHEcsO2229q+XX/6VUMOsy+nla9fr8tpFBcbgE3AaVABK5QWQmlmpmLq1ERqeettrlrlUFEhGTbMJi/PoGvXCEKEUEoxaFAza9Y08umn6fTtl+F7FnTZlE8+aQSqGTQoDcPMR/mxEcPQpWI9z/PbW34pjYB3YWCaJs3NLnf+xxcMHGhx+hndcRwPw/SA9UhVgB0q0/X4FCSTqzDMTEyra8q7YZoWffrWc9llNRw1MJu09Bxc1wN0H0wjqOenj9WybFzp4boerufhelqLKRGP63OUkUksGmPXrl0UFxVj2TYhy/a9OCZKanVyT7YUOA6OVYfxWo5dK2MrdldVkZGeQVZ2NkL5SvimheufC0MYfnFlUmxe0zB1aMkvO6Tbez63RWFZti614o/Dpq8y7no+4RcNOhxPH7shTL/4M/TpeTMDBgjuvlsxoF8fLDsPKRWhkE1JYYdUkWjLsijIzUHQHVcqTMukuFClQpnCl7Lv3rncv5UEBoqy4kI07Uxzz0qLClNeLEMYeEGpH6G9U927dGbHF1/Qp3t3zr3gAh569FGg2BcH1feT8N1kuoYfFOTnY/kq90opTagnFZcjJzs7VeNSh+halO11jUFFRkzz+YJwXiRsp0CAEBAJtTyCFIAyQYRSv5rUr0ekqNngy0CkWhiHi7QGoc+2PuzUL/IwHtpXancZbRrwz/C6vyrEmvruMDD09fNKS3t8077ard3a7Vth3xkglZWVhgKSSY9wxEZ6kozMGInmME1N0LX7K5R3PA2AlRtXMXjYpXzywce8Pu9qrvvN9YRDNpVVVYw+ejj1dXWkZ2Rw7sVryc3JAWGyetUK+vbrj+3XdAsZJo7j8O7SJbz88qlAgh/9ZBFDho9KhVykcgl4W8GDUErJ1O8pbNsmeBFW/sB+6SUX8fSTT3LjzXOYOGmCDh1JXcqkqGQHL7zQn9deC3HDjPco79wZgEOHDnHxxcOBan51/ZsMHTYMEZRDwS8Q7L8Ke67nE3j9un6eDrTd/vuZ7Kn6A/PmraJjx45aOFJASWlHVqxIY8Dgj7Fti6VLltDUNIGhwy6nuOOVdCotRimJZVp06XqQgYP2kpmZRWFhYao0i3J1mZZAu0cJSHi6rMc77y5lz/79YJh8vmoNp578PWKRCIUF+UjHZc2mpUzsfzIR20YpLZCJ0KEzQ+hMK9MwkUq2ACph+sBGIQFXejQ1N7Ni7WscfcwJpKWlEzIMnZ1l+kWI/RFOKcmll1zC008+yUOPPMJFP7iIwMMQ1HYDrSVmGgbvLFnCpElfqTv7D23t5i107lIO3IwQJuASC1uYtq3rwylJLBJOBaqV0llZhgBL3yyYPsgSrUFJKyaa5j23mld+aR909UmDljp1BmhujtI1FUFjBCsFSTQJOsgFkMqfV1KDKED493gg2qrvW+l7e/R6lhl4jYIwbwAC9ZGaaK6c7nEL1zHIhiPIHxU6YBy0FH4fA2Dk+5dS37e2w4P/RiuC2fgJ43nnnXd4e9HbjBs3rtU6Lcf19fbN0OW/Ddyow4/sf3Bf7dZu7fZftu8MkEpLj6KEICqlP0BKPcjb+s0tGmuJh0VjuVxx1c84sO83/PmPd7Ps0+UcPWQI69eto97nGcycOZOuHUv8osOSm2+4nnVr1zJ23DhKS0sBxapVq1iyeAkAN91yM6PGDsMyA45NEgsThIUQIqXUbdt2Kk3a8zxCoRBS+hydVA9bir4q34vQo2t37rzjTq66+iqGDxvKWWedhW3bvPLKK1RUVHDN1dcwZuQopOehJL4so8AWRoqzE7YsQB+PEGAi2LBxPbfd9nvuuusuyjt21KEMAZ6Ca679Bb+45momjhvDkKFDeeHZZ0lPT2fCccfx/ocf0em0U3SpY6V4bfYcpl86nQvOv4DHH3/c5+woP4UcPQAqfI+P5viMGzcOR0pWr16LHY4x+KijNFlXwEcffEjPbt2JhsJ4bpJwKOxnOOFnT2liMALtIZJ+4WSlQY4m50osw2DvnkoyM9KIRkKYJkjp6uLWysX0QUQApgL8IQQgWkjIwtBeKNMwMQwtodCxvIwbb5xBAMKEMKiurmbWrFlkZWVx1U9/ikKRlIpPP12m+TK52dhm22FOyQR4yRQINA3N6QvAjQKUVFimqYsjK31fiNQfqZFT+bxAz3NTXlHLMkH5HCnlH4snsE3LzzjTxG8rOHjVkiQgpc9b871JpmnorDWp/Pp9/vn0Aa1CY6XWxYUNTQwjKFItUh2WKZAU8PiCTLZAnT4ARC18upZQmyFavhcpHpp/PKKVR8q3rwp3Hb5E+Fiv9YKv0on/8lb+d7mk4ktZBl/V6H++H+3Wbu32zfadAFK6KKvC8wdIpVxMIVK6LNDy1gywb89eenfvwbsffMDtM2cy59VX+fSjj4hEo0QiEeLxOCcdNwVTagKvJRTnnTuN2bNns+zTT5k3dy6O41BY2IEzzjydyy7/IWPHjUUpiSE8MPQgg9Tv0dLzfI0oTauWSmEZJqYwUL5Gje5jq2CF0gDBMm39vYQrr/wRncrL+eMf7+Kpp55CSkmfI/pw6y23csEFF/qDltkqcqHw/IET5ZOvTUsXAvbJ2ZdccgnDhg7jissuxzDNlCfBEPCjn/yE+ro6HnnoQT7/7DOO7N+f2/5wFw2JBGWdO+kyJq3SvP2epzguoEnkKTK3EpgCDNPEMg1cQEjFti1bGDdmNJYQCCVJJJJUVe1m0vjxmtdjhfDHZQ0cRAAg9LkVwsAUls8j8VPVTROlDBzpsXHzFvof2Z9wKARKh/EC8rpQwufC6wyrYBDVADG4Z1QqkqOkC1IigS7l5dxw4298D42+D7dv386sWbPIzs7itzfNQErJ7oMHuf/BB7ls+nRysjJAts2rDPrTcgIlwgcVBB4ZAXXyw3kAACAASURBVCLw8kipAZafbYZq8cIYhg5PWpbtFwkWqbBawG/SLiIDVyoMU+A4LgZ63u8QBOsZvtYRgedLgQDTNkFpsr1pmDoJQAg/ZV9vRwYvNUr58TftpQzCisIPKypUG+AUeJwUqlUGn2j5ffgXyQuuX8prpTMyTUNXKGgJfwZXscWzGNxPQd8CGOSicAPYplTKSxdwrA439dWBv9T2gghiKwehPjd8g9rxYf1uM6+C50XbfbUN/mkvX7u1W7v9++1bqWweEDSD/w3DwFBgCQNTCQwJhlI67z0Y4Fu9MNYeqqZDTg6Febnc/cc/snnLZmobmnji2RfIy9OkUf2wN8DTfIRzL7qQ3995O2PHjCEnRxNDE4kkpmFS2KEwFQKRnhZpvOjCS7Atmy+2bOP+v9zHoAEDyUzLZNKEiQhf5bi2upoZN/yGAf36kZWRzuzZf2f8+PEoT4eoEglXK7NLAxeJMuHE753I4sWLqampZeeOnaxdt5Zbf3crrlJav0YIP+1cIRGcfPLJWJbFshWfgWUh/fDa8y++wPiJ49mwcQOfLvuUAQOPYubvZ9IcbybpOrrWmVT8+vrfsLuykmHDh7Pkgw85+pjhVNfXUd69KxgGF0+fjmGZjJ84Adf1eOTRx/CkDs5IAZ6ApOf6Gjoen336KT+/6iqGHXUU5fn5FGdlcv1VP+Hu235P46GDmEKxfftWMrMyyMzO4plnniZkWzz22OPMmzefiRMnkJuTQ8i28Tw9XCQdj/vuu4+RI0eQm5tLZmYmgwYN5t777udgbT3NjkdhcRnJpER5OhjkJ/8hJBjKwPQMjODk4HvPpNK6Pp5Ow3c9iZTgunoYlErheAoPg6SUeIoWMILAdTzAYN+hg2TkZJFf0AEpBYZq+VnV1sIVV/6a0rJyorF0+h05gEcffVyLdyqQysBTkEh6zLrvfqaeeBJdunUnEksjv7CIY487njcWLEQZFsq0kMJACROpTLp160HXrt1pak5y3S9/TefOXYlG0+jRoze33X4HDhIHhbAtlGkg/W5JpcGE9LlQCrj6mquxLZMzTj+DpngcTyk89D3nSIHEQiqLtes3YZshjp00BSVsPGXiKQtH2ThYJKTJwAGDiIZj7Ni9DwcLF4uEBw8+8AhDhx5DdmY2GelZDBs6gvsfeJiEa5D0DFxlIjHY9sUX2IbBxRddTBJJEoGDIImBNC3GT5iEbRrEpUcSiQMklSSpJJ4QOEqRUJKE9PCEBk8B+HNRJFEkpEcCSQJFEkGzK2lwHBpdlybPoz6RpCGZpNFxSUhFY9KjpjFBs+OR8BQNCYe6pgRNCYe449EQT1IXT1IbT9LsSpocSU3coT7p0Oh41MYdDjbEqW2M0+Svu7+mkYN1zdQ3JahpjFPdEGd/TSN1TXFqm5o4WN/IwfoGGuIJ6pqb2FdTS9XBamqamnFky4tju7Vbu/177TvhkQJSNcaUUhimpYFEq4dJqwoxNCaTZHcoxJEKKT0wDBwpqWtqTlWJ99BaMKFQCBfF/Plvce6ZZ+I4DieecCJdu3WjorKC2bNnM3fuPOYtnM+gwQMRQmcT4b9h//Saq3j//fc4fupUphw/BdM0EabNjh0VTBo/nh1ffMHIUaOYNHkyTU2NzH3zTU468URm3XcfF0+frsnSBPXBBNLVvCAlJenZWZx51lk8+cQTLHxrIZMmHUtr11BFZQXz589n0ODB9B80iITP7Zlx/fXcdeed5Ofnc9b3zyY9I4MF8+cxY8YMFix8i9fnzwMr5YPRm/NB4r69e3DicfKzc3AI5AzAEZAUgPC1f9AlerSIoK5ZZwjBo4/+ldmzZzNq9BjGTZpERUUlWzZtZNaf/8yCBQtY+O5S1m3cwNAhQ3QtOB8wv/zKyyyYP5/JU47jokums3PnDqQhSCQSnHXaKSxcsICevXpx1tnnEA6HWfrOO/z8pz9l/vz5XPmznyEMnxNm6EE0ID57SkEgs6C0lwMgLj3iwsZ1PQ2QXR1K9FyJaVokPE3eF0rhJDyUMpCWwPF8wQcFrmeAB1t2VlHSuQdxKbDQgzVAQ4PiJz+B9IxPOenkU0gkEsyZ/XcuvfRSHAXTzr/AJ3tLqqr2cM3VVzPsmGMYN2EiOXl57Nu7h3lvvsn3TjqJe/5yPz+4+BJcV2KFwHU9FAon6XDccVOo2r2b8ZMmYdk2c19/nRuvv56mxkauve46otEoiWSShnqdlKGkpLGhkWQiQUN9Az+7+ipenzOHi6dP57c330JTczMKRXV1DaAIhcLEolEam5rIyMxixMiRLFm8mE8+/oiS0jKUUmRmZqGUYsmSxaxds4bjpk4lMz2DqsoqkskEv7z257z26mxKSks5Z9p5IGD+vLn89Ec/4u23FvHnv9xPNBLGkx5VlZUANDc1sadyN0nH8c+5IiM9nYSffVi5q4K0WExLB0hJJBLRGYaOk5JzSEtPw3NdkokEALU1Nezftw8nmcS0LMLhMJZlUVNbi+e5ZGfnYJom1dXVOI5DekY6WRmZ1Nc20NTcTEZGBqFQiPr6OuLxBJFIhHA4TFNTIy7a+5iTk40QBk3NWivKMi2STpJEPIFtmGRlZRKPx2lqbMK0LCKRSCpU7jguKhb1Na10uFRFwniel9K2UiHblxFpt3Zrt2+DfWeAVOCelyogxQbFan2Hd6sw3/PPPsPyjz/UZF30eknHZcOmzSmOVEIp6hMJ0iyL/TXVXHTeeURjMZYsepuOxaVYtkUsPY01q9cwccworrjsCj5avkyH0lqRf1d8voIPP1lGt+5dcT0HwzBxpOCiCy9k544dPPLU05x+5hmYQtCUSPDL397IGSeexM+vvprsggK6dO1Kjx49MGxLZ4AFQn/Anj1VnHPBBTz5xBM89MD9jB47BsCvHSZ58IEH8DyP7593HnVNTYBgxfLPuOvOOykpLeW1t5eQl5uLMAx+8qtfc8m0s1ny1lvcfPPNXHH11SgpCVs6pTGRSLBvzx42bdyIbZhU7d6NlB6NcS1uunN3JdLUmW+WaeG4jub4KEl6JEZzUyOmMDj//Au56aZbcBTUNtbz5rz5/OL63/De4re57mfXcMtvb6LzEb3oG0+wcesWKquqAJg3dy6zHnqIDkUlVOzaRb9Bg/l4+TKefPRRFi5YwPhjJ3PJZZdT1rEjsbQ0LvvZNfxuxgxeeuYZSrt2wVOKgf2OZP++/SgbQhGbWDiCdFyaG+MYhklmdg71TY0AfPDpMgaNmUg8nmT37t0UFBRQUJDP+++/x8GD1eTm5TJ82DB2bt7Kzp27GDZ0KJZpUrlzJ6CBzJYv9uBJj0XvvM/RQ45m47ZdlBV24MAercGwZYvH1Klw4x1PI0QWtm1x7sXTmTp+DH+4805GTzqOtGiEhKM9FPPe+YCevbojpaKmtg5DCH7xmxv5/iknMeP6XzN+8hSisSjRaIj6+npcz6Oqqooj+vZlzsIFmJaFZVn8+OqrGDv8GO6bNYvpl1yKyAXHcWj0733PdfESDgf27uPiH/yA5cuXcf0NN3D5lT/SA7fSnjogpVOlsze1iOUl0y/lg/ff58UXXuDGm24GtLwBCP7+t5cAuOzyy0mLhLDNbP724ou89upsjho4kIULFpKZlYX0POKJO5g4YSKz//4Kp55yMudMOxcpJcWFRf42o5QWFeP5IUTX9bAti3AoDEBpYZEvZilRaA6dYeh2fjQW0zRBKcJhvU5udjbFBR3wPA/TMnFcD8syycrM1BmbfhJHRlqaH0bVLwuxcFiHMg19LrIyYkE8UHvPVU4b8VQpFZlp0VQ4LlAwl/7+MtJikJfrh8n9/fgK60IEZWCCpAG9vuHTGYLweru1W7t9O+w7A6Sq9h2gqTmO53mkpafjOA7NTU0pbZwaf5AAeOW5Z75xe8+9/AqlHYqJxmJ8/vkyamtqOO7MM3lj0duk2RFcz6Nz53LCIZuBQ4bx3pK3efiRxxg+YjjJZIImXz192nkX0hRPsGPnLv0WX1fL5k1beG/pUiZMOY5mTN5ftoyM9HSWL19OaWkpp5xzDrf++tfMnzeP4aPH8NmqVfQ58kg2b95EXl4eRUVFOI7D4kWLmDJxIn369ePNN95g9Zo15BUU4LgOScfhyccfJy09nVETJ7Js5Sq2bdvG6y/9DYDTz7+QpR9/wp6qPXTu3JmmpibOvewKlr79No8+/DDhvFwG9u/P0EFaGTTgTu3csZOu3boifPKz6XvewqEQ4ZBNKBTGsi1cV4tKWghi4TCWqctW9O7dm1A4hDJM9h48QCwWo6ikmIsvv4xbb/ot7y1dyvqKnSxfuZLRI0eRlZ0NwNQTT6SwtIyt27Yz+dhJzJ07j3AsxisvvkA0FqOse3caE3He+/gjzjrzDNIzM7nwyit46ZlnWPPZCnr27UfP7j3o3KUzGAphaLI9ClSewjItMDQIBDh48CAdOuThuC6Lly6mZ6/ufLr8Uxqam+h1RE+2bttGXUMtn6xYTllpCV27dwIB8+bPBcC0TMo6F3Oo+hDZGelMGD2SrLR0oiGL3Aytsh+JwJVXQveunbDtXKSUdO/aieEjRvD+u++SlZlGbrZOkigrKfTBiyZvF+TnEkgNXDT9Uq7/xbXsrtjJqDGjEUqSn5WdOpZ77p1Fj27dgRae0Mknn8zTTz1FfW0tncq016isuASAkB2ivraWM884nW1bt/LYk09y9jnTWpHa9fCflhZL8dKEEESjURDw/XPOYcZvrue5Z57httvvIBQOYaGoqalmzquv0q1bN06cepwGMHaY557Vv8eZt95CTnYGhgHKMDGNMLffNpPJU47j8ccfY9o5Z2MaAtsXizMEmMLA8msQ2iEtVxG8O2lOHNimifCJ/FJKbFMDKuWDExBY/jZt08LyRU2FACNk+CFWHyQFPKUgw1EIvwy30jW7lYtQJiZgmEJr3HnaiylVK0pCK94daPK4YQiwTQS6NIyR0rBSqfYCn3+J1PxDFfTBzz5VOslGo8TvzOO73drt/7R9Z36JWZnpxGJRFGBZFkpK3Iw04nU2TgLSoi315669808UFuQilFZqxjDZubOSwUOH8pvLLqGqooJeR/Rm7IhRvDF3Pss/1UWOl3/yCYf2H+DoAUchDIN1qz6nqmo3rqPDAmvXruEHF/0AlNKDCjD1xBPofUQv/0GtKFPw/rvvA1C1Zw8vP/cM/fv3Q0nJ5ytX0a9vH7Zv2wZAJBRm5OjRfPzJp1Tt3c+qtRuYfukldCwrZf36dRSWFNO3Xz9+cvXVXDF9Om8vXMB1v74eieKN115nb1UVF/3wh9iRKDvXradbz16sW70agONPOYV1GzezfedOyrt2oWuP7kTCETJzcqg5eJBJ48dTUVFJJKKPIxKJUlRUhG2ZdO/WnbKyEixhEAtrtfiy/A6UFxa3uiLa1yfRIda0aAwlNeH9/vvv56WXXmLV6lU0NTby01Yh2D1VVQw5diKnn3YaKz9fScR/s+7cvTv7D1Vz5llncvDgQRKeR6dOHamtqSE7Nw/D89i6fj0rVq1k944viEWjLF+xAjsUora6mvT0DKLRKOlpaZh+lpcfiURn3SlcBTk+cBk+dAgF2ZksX7GC3MwoymnCSzRy4bSziIZDfJgWpvbQPorKCph87HhCsRBVlbup2r8rdfjCFmzfuZ28jAiFWRlYhsBUCtMfjcvKTNLSPCKWIGxp8rxSkk4dy3gfcBtqCOVkpLSYVq9bzZ/u+iPvLn2Xqqo9qZeEwPZV7iDMCAxMrZUFZGVl0adbL4QMhAEADDqWaVXaurqalsK6/tebN29i9OiRNDY28vqbbzJuwsQU6JAqGLAh0I8yWhHaDSEIhWwuuuQSZt56K6+88jJnn3MOUkmeeuppmpubmT79El+LSnu3VqxYgWEYTJgwPgVwhBCYlsXYsWMwTZMVKz7HtLSGmNZBC+6xttaGP4nQd6Bq+U7LcvihV9fDti2EgHRfnTc9Pd33ugWeJB1dDl4YUEHuoO8RMkQK3HipHIIWnlzQH52IYiKlp71gApT/v/Y66f24rqfV6tG0gyBDNbg+wmfJe56/nS9ZS3JHu7Vbu3077DsDpP4fe28eZ0dV5v+/z6mqu3bf7k46+56QhC0kQBIQyMIOggKyiAyyCTrjODM6jig6o7iPjuPoiDNf0REVQUFBkJ0sJGBYQkAChEAgZN+T7vR2t6pzzu+Pc6ru7aRD4sry6+f1Sm7fulWnTp2qOs/nPMvnyWWzfU4dnm8nm1QqlWy79JKLGTF0MJ4ApS1x5COPLmHk6NHJJD165Eie/f1yCoVGQhc/sWPdOnasW8fSRY/02Qebfm8nXc8pp+Ejh5EKvERhK61o37ULgJXLn2Pl8udYdP+9SRuPPfRg8ndPTw/FYpnRo8bQ0NzMjl27GTR4CJUo4qVXXmHAwFY8z+Oi97+fz37qU/z4hz/i05+5Dk9KfnzjjQC87wOXsOyZ3zNr9mxyuRzFHhsHc+hhh7B+4yYOOeQQ3n3GaXhScve9DzB0+Ah279rFhvXraWpqIvBq62ajNQcfcigDWweAlHi6FsQvtEHGGeh16UZa1DiLtFZcfPHF3HXXbxg7bhxTpk5j6rSpNLdYFucb/uvbVKtV/vFj/0A2m+WlFSuJ71pHZzennHwyTU3N/OqO3zD96KPpcjE9u9t28dMbf5D0c94e92XXjp0MHTyEwQNaqFZC8oGHh8AoZbMjPR+EQCtFY2PBjs/BB9PTtZtFCx7mhOOPZ+1rr3L0lMNoSAVE1Sq7d2yl0NyM0hXyhRyhqvLUsicZlDBSGxCards3MWb4YFIiIiV8jI4SFqSGBvvpESFU6Eg8TWJxMWEV39hSLE888RSnnHYGURRx0klzec97zqJQaERKyXPPLee3v72PsFLCMxEYnzhKvLm52WWomeS24HjOAMJIJVaqGJesWrWKtrY2pk2bxtQjj3QUHKB070wym3ln3U2eF2faWaqGD119Dd/4+tf50Q9u5P0XXwwIfvSjH5FKpbjiyisBYWMZtaajo4MBAwYQpDO2XSmTVLUglaa1tZXt27fbBAEhEl40Q2z1Me6ybNB4r4w5l+GJILFIxVQO8RgobWO4gMQCGrdrsyRrwegyzvjDYLQthA2g0Ajp2bACx/1m7YfCZiu6QsJSeihlHBlu3CfbX8+zz2JMEhu7K2OgazMwjaV9kB5xHVELsKQjvXV0E2JvkNkv/dIvb468bYDUvqYN0cfvg5qb8I1GIvClR7FcodTdycCmAiqygavLnl7G7ONnMfGg8dx+800AfPn73+fvrvkwntYsW/4CDzxwP2e9+91s2rCB1oEDmDZlCr50xIbJxAvSqIQ00BfQ3GIn6wuvuprrvvY1cukUt/ziNo6cNo0BhQKbN27m1FNOxBPw6KIljBk9ivWbNjJ29GiE57N12xaeWLqUjziF1JDNcvlll/Gd736X+Q89zKFTDufhBx9k+jHHEKSyjBw+nKGDh7DkiadIOwvSxrXr2bFtG6fOnU1jNsvmLVvYsHYN27ZsBqC50MTs404gkNZtEkURyhgOOfxQjJQuKFvQvrsdqFkzrMKK42dIqBcAnnvuOe666zecfPLJfOeG/2HxkiVcdvllCCGpVMp869+/TqGpidEjR/HkU08xcvhwurbvAKAhn6OpqcCSx59g8+YtnP++83jlpZUAnHz6Gfzyt/cwf8FCCs1NTJ8xg4fnL2DDhvVs3ryZC847l8MnTaZcLLN9604mjhuJcSn+Qlimd2Wpyyk50Nw6eDBPPvU0mzZt4fBDp/Dbe+/l8MMLKG1o272bjo5ucvkCo0eNxZM+69du4MEHH+aySy5JBkIgOf3UM0ih8WRgwYgyyDpQDxAZ8HAUCCqGKGB9RVaJfu3r36BUKrFg/sPMPXFuwhdltOEb3/wPfvvb+xD4IHwbVC9qzcTgoX6lEf+pMWhheb1ig8q7zz6byZMn82+f+xxnnHoq9z34EIMGtVpgQa1cSWK1wTgGecf/ZAzDhg/nrLPP5u677uLll1bS1t7Oiy+u4IILL2LAoCGExqUES4+mpiba2tooVyNSQcqBkZhCQbFz504KhQKRdoDIAYswrFJVdiGkXfyS9ATt7faZVKJGi5A8k56llIiMBR2esFQJja7uUkNTgQgwrhoAKubrimkThLNYWYum0bpWdDuxfPkYQFVtG75vM0tNpB2zu8QYZes9OvJcgEpVo9EEQUAYRUSlCCEkvu+jlKJarRD4vn1usYkWAOl0GuNoQ7QLqs9m0qTfNrN3v/TLO1veNq+irHNc7Ov3WBoyaQQa4SazAI2pVmlIp4i1z0lzZnPopEmUSmVmzpzJfXfdRfeuHaQ8idCGrVs3MW3aVJQKOf64Y2ltaSHleXYC9rC59YBEIdDErJQecMzMGQC8uuJFspk0Tzz1NJs2bebC953HKytXMXjwEIQxrF+7ge7ObgYNaOGFFauYdMhEejo7mTd/Ael0hsEDB1n3gPT42w9/hO/+93/zwxt/wBFHTEUpxZUfupqN6zcwa+4c2nft4sknlnLEkUeyddNGHrn/PjLNLUwaPw5hDLu272RIczMdbW2MHjuWY44+mlJPEZnJ0NLSwsaNG1i7fh3zFi7kA5degkeKslEsf345AKE0VEWN/TsJdnWB8UprXn31VQDOOutsXn99DSNHjCD2szx8/4NEYUgmkyHwfNasXsPx7zqWJ7dsc+0IXnzhJVa/9jqN+UY2b9rK+k1byOXzvLh8Od1d3Wxcv4l3Tz2CcrHI66tf58zTT2PRosU05Bopdhd5afkKph5+GBqJwilKo8CTVLE8ZGde+D5mnHoSG9vbeeJ3TzF1+nHIbJ6y9ti8s4N0Ywv3zVvEjBnH8vzy55lw0EGsX7eFhx5awMhh40gHroCjgS0bt3HfAw9y+mknM3L4cAJP4nkpKuWo17PZExoUAiqawPcIHaLprkBRWVfXqldfo6VlADOOm01P1SSWUyEEjyx6DIBSqOgoRQhPEKoosaL0mMjScjjlL4WkEtpCkuVqhXK1ijGGcsW6CrXW/OMnPoH0PD73mc9wyolzuee++xgwsJUwDAmCgHQqRaVaJaxWbQmfVIB2rluw7vXLr7yKu++6i+9//wY6O22hwosv/SDFcsUCDM+jq6ubQw49jCW/e4wFCx/h2GPfZQGCEDQ2NvLIggUopTjs8Cl0dHXjBz4Vt9hZu24dnd3dhFFIFEUEQYpKqZQ8Zxs2bSKdSlkCUm3pPFKplKV3iEILUpz179rPfZZ/ue4zSN9nZ9suSuUynifxEKS8gO5ua/3M5XLk83na2tqoVitkMlnyjQ109pQSkthcLku5XKFSqeD7PrlshjAMKXaX8DyPTDpFGEaUy2UymQx+4KyDYYgRgkKhQKlUoqenSCabIZvNEoUhXV3d5HIZhJQEvk9nVydSSJqamxAISuUKSiv8IIX0PPa9vOyXfumXv6a8bYBUrLD3oqdLFHotDkcKS7iIBs/3aevqoFjs7nW8LyWvrXoVP0jx3vedz9e+/CV+9N83cOrJp3LM0TMoFArkcjlmHnUUlUqZ++69h+PedRxDBg2ip9iT9EKFkZ20Ec78DlOmTmX6McfyzNKn+NJ1n2XGiSczceJEWpqaKDTkWbtmHRtXv862bds58/RTXQp1wNNPP4vxYeyo0WgVUq5U6OzsQqQzjB47jhNPPJF7772Xx594gubmZk4/8908tGARnZ1dLHt2OZVKyEmnncnD997DDd/5L77w9W9YFvAwolop89Mb/x9aay68+GLmPTyfQw8+mIkHHcRRRx/N/HnzWLZsGTOPPQakpFit8pUvfol169YB0NHTzY7d7fi+j+f7KBVRLlcsQM3lCCuVJA5l4cKFvOuE2eQKjWzetJlqucyXPv+vxMO/c/tOejq7ePH5F9my2VrIdmzfweOPP8kpJ5/EK6tW8frq1/Glx4WXXMpPf/gDPvNP/8iMWXN58vGnGDFqJEMHD6K1tRW04pc3/5yhg4cy+9jj2N3Wwdb2rfgZj0K+kV1tu9DG0iC0tLbS2dPNE08+weAhw2loGkhDYQBrN25jxOjxPLVsOU8s/T3NTc0YEZBtGMAzTz1HOp1m/OiDWKvXMv/hRYCNddm2dTeN2WZ2tXehxXYkMGjAQHbv3t3rSd3e3knKy0AIzc2NVByS2rG7yJCyploJGTx0BK++uoqlzzzHYVOOoFyqYjDc8ctbWDDfOjN7yhGdxZB0TlKqlG08E1DREZFSVMMqIEgFttYhQDUMiRzoisGV1ppqGHL5VVfhSY/rPn0tZ5x2Gr++8zcMHmyL5Qa+TxSGKK2QSqIiS66poigBLnPmzmHChAn86rbbqFQqHDRxIifMmeNcc9ZFl85muPSKK1nyu8f40hf+jXvuf5hCoRGlDNVKha9+6XoArrzqKoJUilTKZ+yYMUyaPJmnn3qKjRs2cNDkSZYA1hg+du21lEs20WPYsGFJHFFMFWDJOk2vuCkhYP269ZSKRVpaWmhqbkoWPijLjj+wrvCxkDaw3vclYagQniRfKNR50xwXeuxOjQPTY9eo84Eak7CkJBLTtBSaGqhbiyAEtA4a2Mu1OrCu0LEBCs2N1Bki656wfumXfnkzZb9ASgjxY+BsYLsx5nC37XrgGmCH2+2zxpj73W/XAR8CFPCPxpiH/hwd3bZjF8VyiVhReJ6kq7uHUtmav9s7a4WLN27fTuB7dBdLKKXYuGkju0slVm9cn+xz17yHSGUyTJ8+HWMMH/30p7nxW//JqSecwPR3vYvR48axZctmKsUSq1etoqeri8dXrmTtypUIKehwk/mqdVvpjmw2WzafZ+euNnw/4PP//m3+9oMX84v//T5LHn6IQcNH8Pozy1i7ejUrX3yBjevX8sOf3Yb00qzbsI2ho4ZTNSGZTJbW1la6dndy/4PzmTBhglUY0uPd77+EhQsXsn3bNi744GXsKFWYNOVw59Wy7wAAIABJREFUfv/ii4wYPZyhI4awbfs2zr/0g9zx85v53Cc/wZJHFyM9j8cXL2b1qlcYN3ESg8dPYNCwwWQKOdp7OvnQ3/0dC+bP5++v/hBnnfNeBgwcyLKlS9mwbj3HHX88jy9Zgi5XiUoVRKDwAoOOQlSpQiAl0gik8Dnq6BnMnHkM9957D9u3b+Ooo6fTtmsXCxcuYOLESQwdOhRPClpzWc47/VS2bd1G17aBAJw8dxYXXfR+giDg8Alj8T0PIQTnn3EKOzZt5LZbbubRRxYw64RZjBw1irZdO7n5e9/h8SVL+Nd/+zxXf/Bi8vmcZT0XCiEsyBjR0oJGOIZtePgXt/Pzm2/mO9/7HnJghtOOP4qWpiYOHTGIM4618UKBZ6koZkwYhTLKxb5ISuUSa9es5ZtfvJZU4HH8jIM5fsbBuBCd+H2htWDBSLztoMEDSAVNrpgyNGQt5cS4YQUGN3iYBo/PfvpTnPXYI5z37lO58MILaWoq8Mwzz/C73y3h/PPfxx133MmAxjTDB9iUepNJ4zuX0YBUysb6kEvO25ixLt4BDXlacnZ7V6ERgHTgM7CxARob+NQnP8mggQO55pqrOf/cc5g/fwGjR9sMxVw6Xae1+w5w/uhHP8onP/lJAP72Ix9hUKGhtr+AjCf58FVXsPDhB/nV7bdz3MyjOOeccxEC7r77btasWcNFF17Eh664om6N5PGpf/kU11xzNSfPns0F519AJpth0aJFhGHI1KlTWb58OTnp1dZPphYsbmv8gXNsgoEPX3E5ixc/yoIF85k7Z3ZtH09itMCZmWttIBDGkgBjDJ6IkMKzWXNxN2XMPm8/k0oLMaCq+z1u2Be2Tqj0vKTItAV/MZebQngCZUKki8+KIwks51hcf9KA2KMae7/0S7+8KXIgFqmfADcAP9tj+38ZY75Vv0EIcShwMXAYMByYL4SYZIxR/InSWGgkm8+78iZ2YsrlG+hsD6hWegebD2m2K7nGdBYpJEOamjli4iTyuTy5bJZdwMnvOo6DJk6yHDlCcPTBh3LJBRfy/773PRbOn8/zzzyDHwQ0t7Rw3PHH8/5L/oaDRox0cSKCBpftNn7kcMaOHWUnVylobbL8M+NHDePp5cv53+9/j1/eeisrli1lxdNPMWToUA479BA+fe21nH/OmWSzeTQGJeDIyeNtVqL0mDpxHNUoIpNK4fk27XvSFZfx75/7DDt37uTaT/0Lk8eNwhiYMeUwkmweo7n03LM5+8wz+MH//A93334bYRgydvx4Pnf99VxxzTU05nI0NzQipCBSinPfcza/uvPXfO0rX+W+u39LPp/nxJNP4pe33cpXvvhlHl+yhObmLEMGNyarZ6MFTYUAabRNZ9eWpPQ3d93MF77wVR5+aCE3/fj/GD58KFdc+QE+85l/4qgj5wKGVLCbkSPSjBo5mpdXPmlvmtlNJtMOLtPLepA06TT88rb/5Be3zuKWW+/i4XkP0NNdpLV1AGPGjOBz//r3vP/9xwGrKRZxHFwRNvfKxfpgbP1BKQkjmwiwc+fzHD5tDB4r6Om22XQeNhg8rI8wSmq82RzFKLS8V1qX6OiY5ywbpna+5Ls9HlxB4V5s/e5nx1QPcMaZZ3L3b3/LV7/6VW6//XY8z2PGzJnMX7iANa+/zh133GnbcKV5ejl1ksBkN5R1e9RzrRlnHjHY8xqXOHD5lVeSzmS44vLLmDt3DvMWLGT8+PF7AMS9zgrAFVdeyac+9SmCIODyK66ouX6TgCL7cesvbmXO7NncdNNN3OgSBw455BD++Z8/yd/+7d+5oO3aua666iqMMXznO//Fz27+GS0tLbz3ve/la1/7GhdccIEdXTfccex6nGFoA+JxtRN7Zyzaa/ZQLoYSBEaY5LmOMzyFtEHqsUVJysAFu7viRe7dkXHNPxHH4yl3DbZwthBeUupHSJvBh7T0B8pl9cVlnqIodKWPTHK+OIjexOYtV9mgX/qlX946Ig6E2E0IMRa4dw+LVHcfQOo6AGPM1933h4DrjTFPvFH706dPN8uWLXvDPoTasmcLYQNVjZ1dWPniqXR0LCLfcBSebHITeZxOrF2qNSTKcA/F2FvxmTqiu3i7K8aa5DGZXr9hagVX4wybXu1hKBZ7CAJLloiLY6kFbdcfa/Zon17KefPmiIsv7mTKFI8bbsj3uU/fbbHP9vvlLy/vOn4HfjCQXkDE9H0H+qpV+8eozQPma6xvvI8+CWxNt30liS1atIiTTjyRSy+9lJ/dfHPd+WtAtndrIoGdmN4AzYW179X3OI4+7oJ29Ala6SSQG2pcaPE77HnSZdBZ4NM7iN62F7mg87iduI24HeGOS+ChISmuLUScTWcJQ21fhYtvq48nrHXeuExBBAn5p9YapSwtgopT/GLgLWrHxDUE4+Z8ayl9xhgzve+70y/90i9/DflTYqQ+JoS4DFgGfNIY0w6MAJ6s22ej2/YnSzwpWbM7EAMqN1n3dD/75zjNX0Q8aevYVv9Eu9wtt9hJ+ZxzFEp17v+AP0hE8k8IUfedPra5FfgfsU9f+9W+77sfe+3jvos+zhfHulB3fLlcQQhJKp2mUgnp7OygtbUVT/rJcWKvfsdKKy44bDP1+ryuPftT9104F0wvfCD6BlJ7VlDr26G2t/QJyvZzzF7YSPSxbT/yH9/8JgB//7GP7XdfCy8MMXt6fVagBSWi10Ikfue1sbxKUsqEBVzHlh+E+90SdfrSc1QOylqUHPhQURy8H4Md4yxZ1jpUrUYIWSPuDKPQcjyFGt+VpAqjGHQ5SghprU6e51ENo2REpSOnjSLdC2TZ7NGQdDqNihRhGCKlZY4XCLqLRXzfRwhBqAxRGKK1JuVct3EWnx8E5LPZWpWnfumXfnlT5Y8FUv8LfBk7430Z+E/gKvqeh/ucz4UQHwY+DNiYjP2IcG4XYw92fC+CseO/SVjdBULgSWnLt9QtAS0Hju/M5V5ii/Fid4ibzLWxhYRBJKUgTGzowfHCSM9Vq7flMpQGjXBt2bI1pVKZzVu3M2rUKFKpgGW/X053TzezT5iFh7AMxw4YGCHdileAsvwwlmsIYgCwYcMmbrvtN6xevYZ77vkFU6YcxmeuewThymEohUvBlrUazkYgJHWcNrYtT3o2xT2+NuJVe602nR2zZNBdHEdt9SyEVQjaGHzf8WeZGpcU2PH3PMt7I6WoG8O6FXqdsqxWI6tMnJUusQYIiZRxf2LLokjOEe8bf0/64Fm2ailckWEh2LptB4sWP0oqnWbT5i2cftopTBw/xloUtU1Jl65Xoq4wscG5bwQJSaIxJnlubGySSSwZUkgiFdUxa9eUqIYkHsY4PiRL9KhqFAbOfROFEcaNmRSCahgmf6dSASXH8i+kJJPOJJltQghSqRSVSiXpUxAEgKBSKVvm7yAgCALK5bIlhRSCIBVYCgzH1J3NZqlWq6goIsCQy+ZQSlEsFnll1Ss8smAhy59/ngceeIBTTjmFgyYeRFd3F8WirS8npUzOEbpaeflCgUo1pFKpYIwhl7PM6dVqFSklqVQKKSXFYjEpSeP7ftJGOp0mn8tRrlTo6urC8zyy2ayjDrBt5HM5IqUolUpgJI2NBZRSdHd3I4QgnU4TBAFdXTbLMJNNYYymXC5jjM36a2xspL29PbESNTc309nRTRSF+H5AJpOmWCxZsl8syWepVKZardg+5HNEkaKnpyf53RhDT08Pvi9paMhTKpfo7u5GSklDQwNBENDW1mZr/wVphPRsDUGlyGazpFIpOjo60VrR3NxMJp0G+fbJFeqXfnknyx/1JhpjtsV/CyF+CMSMkxuBUXW7jgQ276ONG4Ebwbr29ntO575y61dHpgcNDdMTE5VBI0RNCdrzOA4hg2UpFsJWtXflODzPs2Ue3MoyBk0mihJFWM/GXO8KlbZQhAV4wgIkpYq8vmk32eYBrH59Ldt2pDj5pHPBLxAEEhUZMJYHSEhJZDRKG+s+8X0qkXaEfxaMvLr2Rb7w+a+Sy+U48aST+fZ/f4+ybnAACVKpgGolIippgkDieYIwVC4oVRMEPtVqZFfsMiLwPRARkQpdDTMbIxYphTEGX3oIKQjDEN9Yl4fve4ROkWPdCW7MNEIbgiS9O3KgzsfzJMViyQIwBzhi5e77Pvl8ztaLc6v8pqYCnZ2dxEC2sbGBnp5iAg7S6TRCQLlcIYoistkM2WyOXbt2JWnpAwYMYNu2rfipFBrI5fIUSyXKjn9n8qTJrFj5EmNGjyaKQjZs3ICKFKViESkEDfkGtNZUXL+z2Sz5hiw7duwgDEPy+TzZbJaurq5E8dsCtl0opQiCgHw+T7FYRClFJpMhk85QLBUpV238S6z4o8jek+bmJhCCrq4uqtWQxkITlnfLWh883yPwfYrFEp4nkdKjubmJzq5uVGQBGwWoVKrOwiEx2mYVahdgn5BKRtqBvpgvKrYRiYTLKPB9QhORCgIsWzekHahBQJAKeOmll/jKV75CoVDgnHPP4Rvf/GYCzoIgSDLp4rhF39UATKXSICzA0g4gGGMIgsBZgkRiocHY99H3PAcELQi17ftk0mmMgXQqwEDNIuVb61FDvgGMBZ0GW+4mfhY9T9DQkLduSz9euNQkBmTavf+elBQckesbSR3Of8OZrPfXmgN0qKsxaKjFs9XLsGFDe5+rX/qlX94S8sfGSA0zxmxxf38COMYYc7EQ4jDgVmAmNth8ATBxf8HmBxIjtaOtnZ5iMZl4U6lUsuILgoBsNkt3sYeeilWCuUzOcuiUyiitaMw3UK6UqVZDhCfJZbMordxqWZBzK3CbGWhobmyiUq5QqVq+mEKhiXQ6xe6O3bYkSj6PEAHFYhUDlsIgk6arp8gLL6ygXKlSCascduihNBXy+EIysLlAR3tXYo3INebpdqtOoQwDBw6kWq3S2dGJNpqGhgabLVYsghCkUmlSqYCenp7E0lEoFBwgichmMuTyOcqVMuVSiSCVoqW5QEdHJ5HS+L5HOp1GSgjDIpFSpFNpfM+jUq0QKUUmlcb3fXqKPWSEzV5LuxW4Lb9hCxfbgHCFUZpMOuUsCyFB4CdFbu0xHnHhW+XAWly3rbu7iyjqQetumpoG0NNTxfMyCOHT0FAgDEOq1SqeU6bGGMfNk8bzrHIulUpYQtGQQqFAT08RP/AR0mZ3lqshkVJoA/l8nlIlTABEkPKQ2EypwLfXqrUBpROFboOXXUyOA2yWCbvG8xSDdLCWzkhZ8BeGIb7vJzXVrPUyttCRuKsipWqxL9JzriOcNc6VblGxda+ulIt7N6QUlpW81ztbC8CulVRxFkXXdvxbfF3WKhsTUhpLJgmgLfDzPM+BHGup1A6IxxK3E9eSs32zz4odM5LMNiGFi8kyrl8iAdWeJ+uur/6aAVOzcsbvkbXUudIy1qyZxD0lLkTpIrC0Nfo5vtPECvvG8sYcdsmYJ//9AWL6bjspxv4G5/IBKfpjpPqlX95s2S+QEkL8ApgLtALbgC+479Owc/Va4CN1wOpzWDdfBHzcGPPA/jpxIECqo7s7sSwEQZCY/MFWeM9k0lTDyMU+CLKZNJVqaCd9rck35KhUqkRK4Qc+6bStGh86gsFUEDguGmudSvk+URiRci4PC1x6W6TibDodKx0hbHtOOfqedeOkPGmrvsuYOdkSWPopn4rj5fGldc3FCjSO/E1KQgBaGcttExkCVyWkPq4kjgfRGBe1XHOH1c/LRuuk2rxxgfuB7+PC+e25tM1qihWrVdY6iWtJ6qVhXXWxEvU8GxtSb+2D2N1Xz/kDvi/ZvPkmXnv1Q3vdbyF8hEghZAopUgiRQkr7KUSAlGn3mcKYAM+r7SNlkOyLSCFlGilT5BuPpbnlLFuKw12TxMbgSOoCeesCjrVWCUiyY2dcCFStNEkYhsm1xccp5WgTHOjEuYQFtWBn5ayisZtQGY3n984yqwcnCJLnr5dlVNTR1SYG2djiROJCjI8TzrVss9pcCRa3v00qs/c6PsaWLBG976X0EpdmfJ9rY6YTEBM/T/bCXbyS0g5ImV6Fe2td7w0iBPZ9EXXPsTYkoDQ+MuaMwoGsGKzFsV/WrS32GKe9Hj13TO15sDdoP1DqQJBWryvaT3P7Ox+497MfSPVLv7zZsl/XnjHmA31s/r832P+rwFf/lE71Jfl8nlw+n2QWGaPJ53N13w2ZVBppRDJxZv0giXMyGlLpjF2wChcsiiQdpJMVrvBtTBESpDBs3bKd1tYBpNMZ6/ISdRk4BjAhKS+eqa1STKc8IqXRXhwgrUkJBT4YrRJAhgfGhPiBsMBH29W48F0smDZI36VCC6vsjaeRwuB59ncvDpKOa5NpDWiMMC4ot7fVILY0YASSIE46xCCQyuAn8VkGgSQ0Oql9FsdtSZnADTzpgFsUW3CEi6eqBWLXxzlpBxz2zKKy+wVs3epz8cUlTj8dPvOZCGMi0EUUsHEj/OAHsGIFtLdDPg/31koY7lNOPBGmToXvfAeGjfgkzc1nYLRg+7bt/Otnr2PRggVs2bIFrTXbd+2iubmFzs4uvvD5f+Pee+9h48aNKKVY+vRSjjzqqGSsk5g9bQj8oHesFDVAYepi3zAOiBHH6dkkBHBjZn1UgLUMIYR1VWsLtpN8f3f+2A2NiYhLm8TB1BYoaRdHpxIQRxwHj7GPrLbPjSc9lDYW/Gssi38yig7suWuWjvRSUAuUtvdQ1ECWA1FxcDkGy4tkwHfxcxjhttuOJRY0hzTj/hqD5SqTNQDvcJiLJIytUCTR+kIITAyCnVUqfnR7xdZpQdyD2rNY104yZnumAfQWU/f/G+8o2QtICZIxtK1oxF5pB31JX0WN+6Vf+uWvLW+baMWY4yeZb4QFNskqXSTTuf3d2FWk1prdXbu59ZZbuezyy8hmc9z2i18zc/p0DjpoPEaFSBFYt4If8Pjjj7Nl2zbee8453PCDG7ng/PcxY/qRhEo7ThpDT1c3S5Ys4fTTTqmBFHduAaR8v+Y2MTWFgJQgpL0SIRBO2UjiWd5ZoJC1OdLFSnhCYEkDY94c02vytUMiMKamFDzh4XnUAUATa9FEibGHa8N+tZa0YA8F4yX7ygSsGqyVLIEXwl1brHRFfKw7Lr5KYxx5pt0yYMBFtLd/ADibhoYLeOjBCXz0o1cTpAR33nk737vhJl5fvZYL3382mYykdWAL4w66kCOPeB/pdJonn/4B8+bdzwknzKQhF2B0lY2b1gLfxPcLQCfp1MDk3nzkQ1cx7+GHufgDH+CgCRMQQpDLZPAEfPa6z3DjjT/grLPO5tJLL8XzfUaOHGXvv0mMHLWxdNd+0twTWbx4MQsWLmTO3DlJTE6sXy0PVDIEFk8LkTyy0o1rDDZJzlEP1msWrdrLYOr+rt3vGNB6Xgxs61cBtd9j95Z0HFVGOkCWtB5bpsQeLdSeQT8+tg4A2Q9Z9546622MXdzQ1CIf7acz+jkqCPeUeSbhvjL1l13bxVrV6qxT8fNm4rGL74WoGy8h9nKjmfqhTbYciIWoNyDqS7RQGPaXvivZE9zta69+6Zd+efPlbQOkhNj3JLWnKwBiz5ad+J9b/jydXd0sXbaMQw89jIfnL2DEyJGMGDGcVMrGtCAFpXKJu357N+ecdx7rNm6iGkVMnDyZ7Tt3kgoCMtks6SBgx84d3HLrrcydOwc/qLFJ99UnT9RWjb3Vbh8H9b2p72ve1/Z9/LDX9v2cKFnp77cfezcs9vmblSS+xblRY2lv7+D++x5m6dPL8H2PRxY/zbnnnsMrq3bw8spXufLKq/ju/36fhY8soqnQzMLF69EmRbkCucbTWbvxZc5oOo/WAQMQ2nDTT77BDf/zfSZPXAT8iiAYkMQuzZ83j1NOOYVbb7llr/7dd9+9TJo0iXvvvafPgam/qj7Vq9jjmRTxeEJNgdP7EwuE+5beO+99j/c+rjfQ2tdn38f35VrbR0/eoKd9PdwCb19dqEHxvl4m+rz2Pv6up0mJX7JeY9HHPHJA71xfBF97iUxA574lJuh44332j9sOdKbol37pl7+0vMMWNabun51oNm7czE9uupkxY8bzzLLlPPPMC0w7egYrX1nFU08vwyARXoA2gt/ecx9dxTJHHX00Dz7wIEOHDGHBgvlcfc1HuOeee8mkU2ijyecbyOVyZLKZN/Ni39YSZ+PVB/tOPWoqU4+eTvPAQQwbNRqFYP2mTWzYtAmAwUOHoAyMHD2G4SNHkc03JMHsbbs76CmVWbj4Udra2hFC0DqolUmTJjF4iE2/933LeL9161a01gwfPrzPvm3evJlhw4b9hUfg/68i/or/DkDMAfw7wPMpBEq88T+QYLz9/BP77dSBuf76pV/65a8hbxuL1IFLbzdHNpvntNPezYoXX+b8Cy9k0eLFnDB3NugqP/nxTSx8ZBGXXfo39JRK7GzfzcjRY9m+s43DphxGe1sbR049gpaWf2LG9KMsEaDWNDTmGT9hXP+a8E8QKSWRC/QHWLNmDT/88Ve49+5fcuxxs7jkg1fR2bWbKYdMSvb5xte/zje+/nUADj70MF5+aUXy28Fja6wbH/ibv+GnP/kJQ4cO5bRTTuXoo5v41rcskBo3dmxSiPmnP/0pP/3pTwG4/PLLWbt2LYsXLwZg8eLFiXVjzpw5LFq06ICvrUbu+faXPyiGer+NHYg15s90qj0+9yUHZo06wJMeSGPmQJ4Mzd7UrH/MyfqlX/rlryHvKCBlktVabZIpl0rMe3geZ511Llu37mDGjJmsfn0d+VyGj3/iE7z0wgt0l0o0NbdwwYUXcfudd5PN55k5czq//OVtvPrqaxw8eXJCGCkwpFMpph4x5a+kEt6ZEhNHxjJmzBgOOfwQ7r0bKtUq8+bN44x3n8EFF1/C2jWvs+ypJ5k1ew4nzJnD0qefZvrMYxn33HLmPXAPvudz9nnnUyz2cNDECZzwrndhjGHiQRMBbNA6EPgt/NPHP87atWv57+9+l6lTp3LuuecCMG3aNHbv3s3cuXP54he/yJgxY7j88ssBGDt2bK/A+H1c0F9glP6yciDUJ1AzysRxb3/sgy9MLd5qf/3qy11/QOegDkTFMWXUAt/7OkLsMQ578sXVWt5zU902Y/DEvsa0trgTopYwsG+p2ycJcNy7O2+/J65f+uWdKe8oINUr+NR9LRSauPKqq+jq7GHSpEk8tXQpI8eNZvGihTz1xBL+7bOfRrpU8N1dXVQrFW655ZecNPd4BrW2olTEr399O9VKhY9//OPWvacVRx417e2oO98yEjOExwqzGoZs3GhdeA35HBe+/yJamhv59//4T77y5S+y7KknOWHObK793Gf53Of+jRNPPoUjj57J0scfQ0rJt77z39z+q9s479z3MHLIELRWjBplrVQxkPKDAXy8DkhNmzaN66+/fq++ffGLX2Ts2LFcf/31iQtSuZpsUFO0tXps8h2l1PasN5cUPaY3MNGOjCmhzDDasf/rhJKhVzaaqzMphKVGiJniEaCVxnNZqmFYxfN99z7bzESlFYFvSUKFsBmpcXC6JeN0/FOQ1KaLyR/ifllKB5mgLZvYEVOOCJcAYTNQ96RlkAmesf3RWtlKCS4jUWuNh0YmmQCuZqAQGKUQvg9aY2RcCDuGV3tnsFqSe5+kQoCx7PvCkn/Z9rVB9ift9Uu/vCXkHRYjBXuu0xYsmM9zz/2eQw6dyJAhTax46QW0qqCiCkccfiietAGwAujq6KCnq5NDDp5ES0sLr7zyCsZorvrQVcyaPYsg5aN0xPPPP8fXvvY1Nm3c+OZc4jtAYqUVS6VcplqpldRoacqzY/s2dFShbeeO+CBeeXklpa5OomqFIYNaHcu6oqergzWvraJSsqU7tNZ8/3++7w5zQMrFSP0hUs9qv+e/Pa0PMd9XPUnlW0Vq9e1q//raDtSyDcECAeIEAZK/I1ubyNa7FJaeAyz/lK2BafdTLlNPGY0CFILI2Lw14XloYQlCjfQIlWX5F75PpOw+WgiMlEjPJ9QahSDUGm1sIfNIG8vF5km0EChjj9EIbOUlC0CUsWSdkVJEkSJUthyOcrwICoORYKSw/TTG9s1YJ1vSrpD2GM8j0hZkKsC4jF6DxhjlYtOti05IQCuMComrIGjsuBkJoY4oVSsoo4mMxkgPhI8WHhqJxgMvBdKnqsBBtr/as9Mv/dIvbyzvLItUH1lJ55z7HpSxpIXKaE45eTbpIOCsM07n4IMm4LlU/JTnMaCpiXPfexYTJ02is6uD1a+t4uQT51AoFDjuuOPArSQPPvhg/uEfPsbQupIN/fKHScx2Hks228Pfffhk7vjlz8ll2ti04YdMmDCaNasfZ8JYG9PU3fkYOb+HT/xjgCfvBKHA7MbzBB07/oHjpq+k1LGCVd0GpSqcfGKJfwUssb7A95v26seeYGivjDVRK59Sz8mVEGWKWtRPY2MjAA2NDXu5kfaV5fbXlL6udc/r7auGoamzmMRZddLzrOVIekRRxRpJdAhGIaQhrITgQAUWEtnadMIACqMjCziM5c3SOkQKg9IhSoVoFTqLiwYUURTiebjagAZt7LmM0URRha7uTpoKDYBG6wiDQpj4vFVLy2EUxtjz2u8RWtt9DBqjI3u8icBoBAptHF2BUWA0hlobGFUDTiay145Jjo+vfcoRjxAEA9HCc9Y0gzIKKWytSiMMMjBoIRMAigEhatQqljNLIDzf7WfeaZN3v/TL21bewe+infiVivACH2MUSlWZNfsYoir4UuB7wvJIYUkGmwuNNBYKKK1paW7iP771LXKZNGFYJeUHxLxUmUyasWPH/NFxHP1ipd4K0tX1CDu2PQJAd/cSJh/kdvJgtIsj7+xYzK4di3u14YxAVMr3MnIEdHet7vNcnlfAmBCtFVHUDYDWZarV7YnCM07x2t+K9PS8hDERSsWgIKK7u4tMJnDko04RC1vjMZ3pBCAKH2f79lcSZdpQr+ihAAAgAElEQVRLEdedqwYk9vdd/wH7vtF3vdfve+7T1/E6AQ0qAS+18XrrRAq2bX+ze9C3bG7vIp9voqu7B2EU2XQGz5N0dLYBtnpAPp+jo6MTIQRBysOT0N1TJIpsjctCoUB7+26ElKSCgKGDW0k35N/sS+uXfukX3kZAShtdR4tXY5HGQOB5SZCpAaQ2oA1CgvAkFRUBhlSQwmhDOrAWBhvTKazJP1RI30cgkdJQ1coxqRuEB1qAFB4y8BIOJCE9tIljGlzoQt3fxlEv1wfYxvYLTT1zjrMCoJLVrDbR3grYKOcOUwihHDjU2Go87ruJMG4Vr41Ca7tqtqvlyK66hYvT2FNR1ilLbeyKXqPQOkKg7X7uu0ysDbq3YqZ3W1orl6q9h4I29lqjcBue1wwIpKeAToRI4fsNyQh5Xgh0ImUGz8/agXTLdiEseJEylzwBVtHrROEDKNXBY49auoqtW20T27ffxhNP3Nbn89bZ+TTLlh3+Bz2jkWVZYMvmj1Hs+YMOfVuLMcJaT4RnfVVILGGuD0j7jiGR0kcID63BGIkQHr6fIgztuwQS30+zZcs2srkGGhubCIIM1UqElD7GCNLpLAaPSiUE4eF7Abt2tpHJ5snnG/G9AN9PUQ0VWoMnA7LZPMVSBWMEgZ8incnZQt+hQuORyTWwbftONm/ewoTxE2kZ0EqlHFIpVfCDNA0NBRSSrq4ehPDI5RoReFSrERhJvqER8OgqKirViFy+kVQ6x+5N7wPTSUdXN0YUKRaL6DDEa/YICCgWy0hpC3IbrNtRCEEgArzAxw9srFgqlSKXbyCMlCsI7pHO9FOv9Eu/vFXkLQ+kKpWt7N79CJGuoFFgDMopc60ipNCoMMQT2lK0mAhpFMYBiJAI7cz2RoW079pJU1Mjvi+tmV4rEMoBhcgdZwFC/J0ECOiaWb8eMJioTnnXXBZxm/Wrd1MHWmrb31or+zdL4nIpxlSJorZku3LbtS6jonKvY2wBZtD6jZGLraZnlbWVCtp4aBrsduEhhYellN8EpEEMIxWkqYYajaBcDjFa0JBrtIAAiZ9KU6kopPD5+D/7fPq6LJVyiBQ+SkMmmyMMNVoDQpBJ51DaXpN0Sl96KUrlKkJIAj9NLpunVK4ihY+QPulUhlK5SipIY5CkMzmUNqjI4PkBmXSWMNQWnCBt3UHfJ1SGKNJUQsXAllaq1dCWWiEgl85SLYcYIZF+gJfKEGqDNgJpJCk/TSB8olCj8Uml0uzu6Cabb0B6PkIGRNpwx113c/bZ7yGXTpHL5ahUq0mWWSawxayNCwaXKKTQhGGIlD5BkMLzA0rlMiDQStPe2cEDj9zCRz78YfL5HL7nI51bS3oiYdcvh7YWYKXUw7x5C5g151QacjlsaWjryq8oYwPMY4Z3WStrFC9tlDFEwKChbbz82p0cduSlBNLDF4KU54EyvPLqKra1b+fYGTPJpDMYo5FIVwORJCheAaVqhXK1yvZdO/H9NFEIE8cMI5tqQQ5uQQuTcHsOaW3o9YwOHpgnbk4jGNTaO6avqVCzQPXbwvulX9468pYHUj09y1m58pI/a5ttu/6szf0ZRUKi7D23qpcYt8oXwkPKAFuyzpab8bwAowXGHRf4KWclEwhpi/m2t3cghEeh0ITvBXgyILQRvniej5QBShmE8BDSx5MBShuMcW2mMoSRRngBGEEQpDEKq+idFUEIW0wZ4SG9AGMk7e1d9PSUyOZyNDQUSAVpAj+F76XIZfOEoQYkWlnAkc5vB66gsXAqkw/7V6T00cqwq3058Pe0Dv4wy1cPZfKkQ5k2ZRopGTB06HmsWPEKh0x5hYbGRjQ2bkkr44BTgULTbGaesDAxFa55fQ0wgZ2dM5h41EM0NzQQGGFrBgoAn0LTsbzr+EeQyqB8QRnN/fc/wIyp0xgzbARCQBWDks59LCRbNqyn1NXDpIljaGxoQBuQniDS2gIWCeVKhO9LVq9ZR6FQYMDAAQhpi10vWfIERx5+OAMHtlALcLcl9jo7u1i/bh2TJ0/G8328uJZjnCUPtswR4Pse1Siiu1TkvgcfZMOmLRxxxFBmHTsL3/PJBgEeuKopgghNRSmkb+vmSQCtEdrWbAy1YeOmzax8ZQOnnXqKff6kYPOmTYwePpKhA1tJY9vKpaSr82iSGDKBzdjTKBCQzzVgsIWjMYJMJovv+1SqVVY8+QQHT5rMoAEDbMYkkEr5Sa3C+HpTngA0W9raGTCghVwuB1I465gNNt+xawdPPLmUXCbDscfMpFBorA0WuDgkgYoUrQMHMGhQK21tbQwfMtRmyQnJ2nVrePH5Fzj2+GNICw/CiJTv21gxQwKkjAGlFHk/4Jmly1i7bi2TxtnT+ICnqVVo2g8KMrBfIvV+INUv/fLWkbc8kIolCIbS0DgXg0D6aaS0mT3ViiKXyRH4KUJlV5hpL40nfCLtsoSQ+H7AKy+9wvjR48m3NOPJgDt+tZRPf/Ln/Pt/Xsb7Lz7RpXl7COHhaUkgUyiDS7v2AIGUvnVhIDHSJ9Ii+V16zpXhQI9wbQUisInWQibWD/DwPZ8YMEVYBVSrZl8LT9bJpwYpkC47SsYBwcLWIvzwFVdw689+xspXX2HE6LFIz0NpuOF//peT5s5mymGHIIwtdixFXWFdo5HScxlwGmPiwrQ2Q0mBS2kXlCoVUn6AL2whXk9olFLu2l2ektI8+9xynvn9M2zfsQOlQsaPa2L60UczeZJ1l3muCG9cSk5IQXdxDQDZ7HAGtBxv3blGk8vH1qYmtm/zOf3UY0mlBvLtf/8mzz77PAAXvu8yTpg1i1Q6xZQph3POe9+LELXMJk9YmoJQaXy3OR34NOdypAC0JiVFMqYCEEY71yzs2rmTcrnI4EGtthCIsb9LKenobkNqwzVXXMFjjz7GQw/dzyknn+RAlHLKM4XSgiDl09XVyY9/8mOuvPIKmgc04wmPNWvXsmPnNpoaj0G41H6BsfkNWrNx3Rru+PUdfPraa0kHEqNcsHhSsNqQ8lyGnVaE1SqLFj7C2JGjmDNrFs8++ywPPPgg5551NkYbtDF4QoKwtrq05yd18Iw2SAOnnXoKjz76KF1hxOq1a2gdNAhPSjxsAerXVr3G6GEj8I0gwNRMirgagnGqX8x5JCQaQ6Q1xIHVAFISGUNk4JVVr3Lh+85HKwcKPS+uB94rlSQbSGbNns3nv/hlhg0dWpdlaCkQiuUi9z/4IIcfNoVsNsMzzz7L3Lmze/NYOVd84NsCza2trezcuZMRw4YjhGDDps0sXbaMM08/nfPOey+PPvYYKoxsmqH06qxR9jMQHmEUsXndeqYdNoVi0f4kNQkIPFAAJA7EQN2fuPcHiRDCAIuNMXPf7L7UixBiLvAI8EVjzPV/pXO+Jcfi7SpvGyC1bv0Y/uaSXzLjmGNY9MTjaAQrVqygraudQ6Ycx69/8QuuuvyDALz48irGjRmHkZJnXnqBrkqJhmyeD1xwNL7vs3nXdoJUQHNLDvg5zS2zGTLsUmLOGQykjY90GkA714SQvadBLRxwcLw7cVFXY4xVyE4pxSv0Gjyy6eLS8elIKeyKlRpoSmKs6pj3VB13j+/OE7ft1XZDylot4q3bthJ4knHjxjrrgFNQMnD9xCo1o0HpuiK6rqgwFthVqlXuufc+SqUSkw+ayDEzZuAJgTDgS2l7LuyACCk5etpUJk+ayO23/5oTT5zN6NEj8DwPrSNbRNeBEOPAlFYao2Kmc7t837F9O5s2b0rKyOzctZNjDpvEsCGtTtFYbXP2e87i988+x+NLlqCU4oMfvJT3nXOOzRJz42mcUhcCqo5RvaWp2YKn+H7FwC4eR2GvXSnFq6tXMWb0KALfQ7r+Br7gtfVrWf7Cs5SKPVSqFQCee+45brrpJ/zud0vYtm07Q4cOZdbc2Vz3+esZO248q9etZcfO7TQWGhESKmHIipdXMHXqFALfA2O45qor+fnNN3Pj//0fl11+ORs3bGDm9KPJ57I2Pg2d1HHUrkB3QqMm4KUXV9CYzXPU1KkIz2Pu7DksWLiQnW3tjBgyCLTgqg9dwc0338yrr61mzLhxSEApje8oH2IpVats3rqFww8/FCVAGigWi5R7eph8zEwCi77s/ZCeVfCO7yjpVZwVYNwdcSA3UgrhMiFffHklo8eOZcjgwdaaZez7IzyZWH1qd93e0/a2dmbMnJlsi4xCCEmEZvTo0Rx55FQ2b97MunXrkmtKxsmNldIGIQUjRo5kw4YNGAxbt2/nmWef4bQzTyOVTtf6i8s43TNB2D1XPaUia9atY+XLL3HXb9pZswY8bzyTJk3m7z/6Ma64/PJe3Fr7krXr1jJ+gjVpzZw5kycef3JfyS1HCyE2GmNG7rfRN5A/VbEKIVqAfwDOBiYCOWA78ATwQ2PMvD+lfwfYh7UAxpixf+lz9QsIIfLAVcAFwFQgAF4HbgJuMMZU93P8ncB5wD8bY/5rH/vMAB4HNgJTjTGdf74rSM6xCJhjzAEV1OxT3jJAqt4SopQNqqyXgw8u0NLSwrPLltHR0UlDocCmLVsZO3o0SmsWLXok4fZZ/OgiJkwYjzKGLdu3MXr8OH51y62EYcjs2XNoSKUtCHEzamAgcKZ8pRwPkOM5EtJWr5fSknbWj7Q01hIkHDGfUdrx1ESoMCKfzyVMfiqKkmuyE7jAuHgOYcA3KgFdRmsH3hw3kXOPoA0pz3ffrVUBZRzYU3z1y1/m2n/5JEPdCj1UipUvv8yhhx9KPp93QeLWMmN0TanEIENKD6UipOc5UGHPE4WKxx59jGo1ZPas2Tz+uyVMP/JI0ukMQligIRAIIWtB90bQ0dFBodDIqFEj8TxrwdJYdx4xD5Mw1lInJBPGT6Ac2lghg+G55cupVKuc9Z6z6aiWuPPOOxk7ZjSes54pZwH5x49/nNmzZ+FJd9+Uwjh6gkhFlncIrBXP82hsbuYr3/42V152qVXQWhNzXysjiMLI8Q5BqCLKYcjGjRs48/QzHMDVeL6kXK2wadNGTjh+Lu1t7Vx7XQuHH3wIB0+exPBhwzn9jDMYMKCFxx9/nFtvvoUH7nuAx55+mtdefZVhw4cRKUUYRagoYufOHbS2noDBUQ/UKc1qWGVXexsnzp2DMQbfiwO47R0UJuZyspbFzq4uXl21irPOfg/ZTIbIaIQn8VMBfuBjnMswPod1oRmLg9zzaYGP/b2n2IPwPXINjRhjn9p169aRzmTJZDIgBZEQaC0IowqBDIgDkRLcJABlRzmMIqTnY6TlUxJAsVRi6bPPcuZpp9lEEqXxPYl2FlpjbAKJBfj2yiuVKo1NBTzPUpvEQEMZRTad5fjjj0cZwwsvvcQhhx6CBiKtbZyV9SrattyCpaW5mWefe84CoZdWcsy7jiXX0GhJMd1wh1KgVIRvBFKC0gpPSDwhCH3Jmq2beeChB3hqyRKOPNLjnHMgnT2XO3/9Wz509VW8+PJLfP2b3+xlUU4Wa9K9I1pTrSsRs3TpUm657Re8/wMfqE0+xhC8RRx8QojZwB1AK7ASuAXowgKqs4ALhRA/B642xlTetI72y59b/gW4HguWfwRkgAuB/wSOBS7az/HXuP2+LoSYb4x5of5HIYS1dNhp6YN/CRD155K3BJBSqsQLL7wboC42xE4S1WpMxlhi9pw53H3XXTy2aDEnnnEGXT09DGhtRfoeC+bPY/joMXR37GbhggV86OqrWb92PZ4f0NDQyMrn7T2ae9JJiDheIV7hKo1nJDpSBL5vAZGQCE9awr0oQhjH0py4KYQFYsYlv0kBwkOFVRY8PJ9ischFF11EGEaYQCB9n8gxKyexL5oETHgo69IxDox4sfJQFthgrUgqVNYKpTWe6ysChBGMHDKc4cP/P/bOO7yKauv/nz1zzsk56b0AIfSE3jvSERtFiortWrDfK1473qtiR6/lvvaGolhRVBDpSEdqCC0QQkJ6COn15JSZ/ftjT0JEQLzl+b1/vOt5Ts7JzG4ze8+stdf6rrUSMDUw0HA3NHDs+HGmTJmM3xKWXDYHPsMHltbBNK3I3EJhVqRljlQveit4oGmQfvQok6dMISw0FK/Pi0SZUTXkabOeVBgp0wprcPDgYbp1766CFWq6ZebSkQiFHxICn8+PTVeCoikFPr+JpmvUu90cy85h0qQraPQZNLgbySksZsL4CQogbDFjwDILWUKuVIKZ31BaBkOaKu6OpXkwTcn+9HQ6dulCcEgohinxeb24HAHWWjTR7TZ8UgnKaDZOlhYSHhWNMzAIv8RiYAJNt9OtWw+CQsI5fjwPe0AQdmcQb7zxFrfeMht7gB2/oRj2dddexffffsu7b71Fr4ED6dmrD1XVNUTHxnEsO4uo2DhCQsOsmGen4zaZgNvjwZCSiKhoSwPaFDVbgbBNpBI4hIbHMMgrLCQ6JpqwsGDcXh82h526ujoaPR4CXE68pokuNAxLIjEF+AyzOVWKz+9HaE1CLxxMT6dd+3YIm47PNPF4PGTl5dKta1cafF6EpmHqKvq712cgfT4cdodKq8Rpja0DgSbVPHj8HoSuUXyqlLLKSmrq6wkKDSUiMpKKqirKSsto1aoVIUGB1Ls9zQKv3zAIdCnPTXdjIzHxcTR4PNhNm9rsmH7suh2f4UcKG4V5eXg8HsLCw3F7GtFQYU8Mw8D0G0ipzPVe0099fT1p+/eTlZ3NzJkz8fl9nMjLJTsnh/LKSgByTpXgDAigoqIcTUKQKxCb3YbT6aSovISdaXtI7tWdF//5GmbdxUizgvA293L97Lu4eMRFvPX661x9883Ex8dTVVWF1+tF13VCgoOpra2l0ePB5QygsrwMgLiEeCrKyvnb3Lm065qMXbeh2220S2xLdNCvwer/P0gI0Q1YAbiAe1GaCNnifCLwA3A94AVu/f8xzv+j/wodAAZKKfc0HRBCPI8SpmcKIdpLKU+cq7KUslwIcTOwEvhcCDHwDEH7FaAL8LyUcut/5xL+M/S/xMpuUFm5msrK1VRVraGycjUVFauoqFhFXd3e5lJjxowDYPPGDfh9PuUO7AggOyuLwvx8uvToQf+hQ9i2eTMen5+8wgLad+jA8ewssjKOAjB+/HikZlOfZoyRzrqNmxh78URCw8MJj4pi8uTJ7D+cjtc0MXUdvxD4NYGpCWobG3lh/nz69u9LcFgwIREhDB0xjM+/+hLhcNCxawp9Bg3Ci8nm7dtw2W08Pe8pdu/ey5RJVxAXHYVd08jJy1WYDilY9MXXjJ14GZExCbiCw+neqy/PvPASDT4DU7NhCA2/qaIrGwIMoeE1TPwCfBKkTeeW22fjsDvIOpGH1zApKjmF5ghgx/btTJk4kZSk9oS6AumY1IExY0bz5jvvIG06PgQ+wCvB0HUaTYm06RhSaXE+//xzHvvrfWxYt5YlS5bw+isv0yo2mtYJsdx662xKKyrwS0Hqvv1MmTSJ+OgoYiLDefrJx5UQqNlo8Pox0Rk1bASB9gAyjmfhMSTodrwmeE3w+P288cbrBDlsPPfsswSGhBEZFwc2G4eOHcPmCqSktByv30SzB+CxTHSVNbUsWvQFA/sNICwkjDat2zB79m0UFJZgouE1DNxeH35gy7ZtvPjU0zz+wAMkxScQGRJM3169ue/+v5JdWIQHKK2ppcrt5lRNDfU+Hxu3/8JnCz6ie1J7YkJCSOmazFNPP83uPXtpHRnFjddczfade4iOiycoNJwp066iuLScguISKmqqKC4tpVOXFACyjmcRFh4JQmP/gYPkFxSSmrqfQGcQmVnZZBzPIisnl9p6hQsrKS0jKyefqpo6sk7kMHLYcGw2O5nHs7n//gfp0L4DwYHBtE9M4o7b7mDfwcP8snsPDqeLw0cyOJ6dTfqxYxQUFlJcdoqDGUf4ae0agm06X3z2GQDdk5MJdNhxBTjo0C6J1P1pZOfnUVOr4m19+dWX/POlf9C1bVuiXS66tW/PvCcf58e1q1nw5eds2bOLtdu28vHir1i8bCmvvf4GMydPpWvbdsQFhdAxvhUzrpjCT8tXUFlVzd7UVNIOHOCrxd/w1TffsGHLZj786CPyi4o5knmcBZ98woatm3nvow954IH76dqlM1HhoSS1bcPVV19FZnY2AGUV5SxftZJPv/icJd8tITsri+zMLF6e/yIjBg2hTXQU/bqm8MTDD/HaP/7B4cOHOXosg7z8fPLy8sjIPMaHH7zPpMsm0rNLZ3p07Mh7r7zC4oUf8/nChWzevp1vlizBkGazebmosIDFixezbdt2tm3fzqJPFtKnYyf6d0lmz45dHDlyhKtvuJ7EpLbN7y2/30/XHt0JCg7G6/XicTcS4HAQ6HLhCgggKDCIsNBQIsLDiYyMJCwsnJBgBYxv3aYNN9xyM/l5eaxYupT4+Hiio6JwWoL/uUgIoQkh7hRC7BZC1Akh6q3fd4kW4EEhxE2WWQ9glBBCtvjMu4CX9+tAEPCSlPKNlkIUgJQyH2XuqwRuEUIMP2OcUgixUQgRLYR4XwhRLITwCCEOW0z2d0kIMdq6hiQg6YxrWHiW8n+oLyHERCHECiFEmVU+SwjxDyFE+IWMr0U7cUKIBUKIEiGEWwiRJoT40+/UiRRCvCCEOGLVqRZCrBdCXHyWsg4hxL1CiFQhRKUQokEIkSOEWCqEGH+BY3xICGEKIbYJISLPV1ZK+X1LIco6VgTkWv/G/F5/UsrVwJtAT+CFFuO4DLgT2IvSeiGEsAkh7hZC7BBC1FjXt08I8eeWa7pFG5Ote9U0z0VCiE1CiLut8+2sdTPK+r/lutn4e2NvSf8rNFJN5HIl0779y2iaaAYKHzo0FSm9CGFn9KjRAGz8+Wf+8nAdmq5js9v5aflyAPoNHUaALvh55Sp2/LKT6voGklyBZGXnkHnkKOHh4fTs0w+vpnb1br8yDf244idW/LiMCZdcwk2zZ3MsI4PVK1ewd89utu3bT1R0lNpdS0l5eRlTL7mEg2lp9O7bj6tvuAEpJRvXrePGG65jz/59XHPzzYQHBVNj+DlVXQ3A5i2b+ceL8xk0ZAgzZ11LRUUFDY1equvquPfuu1j8xecktGrFxMuvIDAoiLS9e3lq3lOsWrOOT77+Fl3X0YVsBnw3mQBCQ0Jo9HgQmqDWQreWVVVhOhxs37WHPb/s4KsP3yM6JpaLxo4lNjaGqooKDqTt5/1332HC5CkEBQbR4HbjdjdQVlqGw6Hc1kNdTiSSOiso0tdffskvmzczZMQIhgwbRuru3Xz6yScczcjgwUfmcuOsq+nTfwCTp8/gyKFD7N65g+mTJ7Hg629o1TqeX7Zvp2ufPuzZtYtXX3mVe+bcR011DUJAXGw8NbVu3nr9DWw2GzWNBifyC/nnW+/SrmMH9qSm4na7aaz9kZCQYLr17EF+YREAz8ybR9aRo1xyxRV079mLg/v3s2jRp2zYsIEFi77Er2mkHthPfaOHH7/+guOHD9FrwACSu/egpKSE4vx83nnzTX5avpxHn3uevfv24TP99Onbh2EDBvH3OfdRXV5ObOtWTLjiCjRD8to/X2Pjls2AwqFhM/CaXmobaqitqlEmUpuNMD2Myuoqli/7AYCImDjaJiaRfeIERcWnqKlrwOM1iItvxaYt25HeRqKiops1ILrdRn5REZ2SUwiLilY56IC5jz7CL79sZ/TYcYSFh7N2zWoWffQxlVXVXDJ9Gsndu2GzUqN4TJPd+1LpnJLC8lUrST+czkUXTyA38zh5J05w4+xbiY2OQQAhoSF07NwJNK25rz2bN1NWfJK+gwYRFR3Nlg0bOLw3lY2Jq7jo8ksJi4mmpqiY6KgY6srKef3pZ5GmySWXXU7HTh0pyMtn+Y/L2LtzByvXrKVvn74UlZRw+GgGN9xwA40+H15DMm7cWA4dPMjFl15CeFgYN8ycSerOncTExXHF1Km4XIGsXbWSuY88pF4YQjDhkonU19aB30eXTp24ctJk1q5ZQ3KXLkybMZOTJSUUFxbw8gvPk5ebw8efLVJx2gyDhQs+Yu7DDxMXH88VkycTFhHBho0b8Xs8rFq6lK69e3PtddcSHxfH8wEqblPnpA5kHEhnxpXT+X7xYl5+6mnate/A0hUrKG+so666msG9++G02SnQdfwmdGrTllVrfqautpbeffowpF9ffD6DiNgW4Q4Mk9CoQBXOw+vHqFNCrNMRwHPPPMd3Xy/mvdff5J477ybGAv3/Di0CrgXyUWYXicKjvA2MAK6zyqUBTwFPohjgwhZtbDxfB0KI9sA4wAO8dK5yUspiIcSHwEPA7cC2M4qEW8e8wLcoE9EM4CMhhCml/OS8Vwo51jXcZ/3/zxbn0v6dvoQQT1htVwDLUbivXiiz1mVCiKEXYnISQkShsD4dgK3WJwF4F1hzjjpJqDloB2wBVqGE1iuAVUKIO6SUH7SoshCYBRwCPgXcQCvUfF8CrDvP+DTUffsL8D1wrZSy8Vzlz9NOP6A7UALsv8BqD6PW0X1CiJ+seh8BDcB1UkqfEMIO/AhMBDKAL4BGYAzwBjAYuKHFOG4H3gNOWvXKgFjU3N2Meg6qUHN7E0oIf6rFmHL+yHX/rxCkmjYxhhHIrl3Ke6akpIQ333yDtDQvNTUQHf0L3bo9QHh4BOmHDrH/4EHyCwtJTTvAsh9+IDAwCEegi8hoJUQv+OhDLp18Jdt/+YU1Py7HMAw6JaewJ3UfHhtUVFSwO20fAMuX/sDrH31E6/btyM8vYMyUKUTGJfDVwo948vHH6TNkCDExMXTr2pUnHnyAg2lp3PnX+7nnoQcpPFmMlJJpN9/M/Mce441XXuGUu4GZs2ZhZGexcesWALZs3Mijj89j2syr8Hg8lulS44N332PxF58zZNQYhk68HJvNjilNBsclYg8JZ+fGdbwwfz633nMv+TnHyTqeyeDBg8nIyEAIwbixY2hwu9E1Ha8Frvb6DUrLK9m7L421PxTYdWoAACAASURBVC5F03X+Ou9Zxo8fQ6ArgG3bthOZ1IH6ujrSDhxkyKCBNHg8/Lxxk+XGrVy8J44eid1hb8Z2bd+0iTvvv5+5j/0Nw+/D5/Fw03XXsW3zZm6/+U/Mf+VVpl11FYYpWbd+I7GLv+CnH74n49AB2ia1YcOmTYRERhEUHMz333zDc/Nfok3r1uiahjPAyc8/b6KwIJ/+g4cidBv9Bwyk1l1P26R2bNyylaFDBjOoZw+2bdtG6zaJVFjCRmb6Ed78+GNmXXWVwpCZkoceuJ+33nidzz5ZwP1/n8eetP1ccvF4Ghvd3D33MSZddgnLli4lJiqK2ppaco4c4dm/P8aSL7/kkXlP4pOSiIgI3nvlFarLyxkyZgwvvP0W6an7uW7aDEqLixgyqL9awEJy1cxpRIdHoAGt4qMtDziNuoY67rxlDkcOHWLStOnMuOZaunVLJjg4CI/PBwg6depISEgwoSGhTJ80C7fHw8fvvA1AWEgodXX1jLxoOLExMTgcdgDy8vPYf+gwEdFRGEB1bS3D+/VjxQ8/MGv2rYRHRGDTVOiFk+Xl+E2Dbt26UVRURFVVNQ++/AqP3TuHvBMnuP/hh+nYrj0aotnM6zX9zSY00+dnX0YGIeFheP0Gb7/7NgtefY01y36k/5hR2AMcHD2cTvvEJB695x6CgoJYu2ETvXr0QDOVV2D64cMMGTGEe+68k227d3PkyFEuGjGC6Mgo1m/exIB+/fB5vQojpmn8z6uvkrpzJwMHD+aRp57G5/UwbswYGhrcjB46BADdphMaHkZ6ejoTR4/hhWefY+2aNdxzzz289vKr1EuT2ro6gkKCmXPXXXy2cCEzZs5k0pTJaDbBRx98iMPhYOeeVGIT4iirqKRN587MmnUNu3btJiMjg2OZmWQcPUpDvdqkhLsCiYuI5MmHH+WD995m6NBhLPl+KREREexauoSk+NYE2uxYrirq2V+/lVuuv4WIiAg+WvAxuoHyklVeEArwrgmElGhSgBToFgZOADFhEcx9ZC6PzH2EV+a/yCsvv0IT1PBsJISYhRKi9gEjpZR11vG/A5uAa4UQP0kpv5BSpgFpQogngZw/6DU2wvreK6Ws/J2ya1GC1PCznOsNLADukCqwHkKI11Cmo0eA8wpSUsocYJ4Q4ibr/3nnKX7BfQkhxqCY6y/AZVLKqhbnbkKBqp8C/nq+8Vn0AkqI+qeUsrm8EOJNq/2z0ScoBj9LSvlVizrhKAHrdSHEMilliRAiDLgGpcEZ3HRtLepEnWtgQggnCtc2DaUdmiObMmv/AbKEqDWAD7j+QvFwUspGIcR1wE7UNR8A4oC7pZQZVrG/oYSoN4H7WsydDryP0nZ+K6VcapW/AyUs95ZS/irngRAi2uq3CrVuRgNJ/47H5P8S0556aZwsKaF7j5489thjTJ06hdTUVAYNgunToWvXUDZt2kBVVSVSStL27aV1hySCI0PJTD/M4OHDiQmP4NLxFxMZHU1mxlHGT5yA1+OhJC8PgIR27UhM6YwzOJjcoiI6dFJ5SAZcdBGJycks/v4HjmZnczQriy4DBgFw6MB+evbsRVhoMCUFufy8aiX9+vfn1ZeeIyU2nNHdujCmZw9G9unN7NvvULijmlpG9OtH7akienVLBqBHz548+cSj9E7pwODeXRnYM4UenZP49qvPsNlszL7vLxw/fpjYmBCumTmFxvoKnn7+GUJCQ9m2bhUu6WX3L9tI6dyRPj264W+oZeSQAbSLj6F7+0S6JikzFUBsdAxx0TGYXi9hoaEEOAIw/T4iI6LIOZGP0xXEX++7l0F9+9K7UxdiQ8MpKSqiVWJrrpw+FYfQuOnqq+naqRMd2yYRYjHUYcOHc9O11xIdEkJcRCRt4uK5+Salme7eozt33XUb0REhBOgmDpuXO+66DYD8E1k0NtQRFhzIPXfcxtTp06iurGTz2jVEOJ1EBLgI1HUWLXgPgNtuu41ZM6ZwxYRRTL/iEmyGhwDN4JJxozhZUU1CUgdWrFhlgY9h4qWXctOsq3EJE5vpwWEzmPfU3wgLC+Obr75k1/ZNTL5sPO0SW2PXdYYPHEReTg6VVVVER0ZienzMvvV2QkJCyTmaQeuwaIb36ktyYhI/LlmCEILnXnqRwqwsundNxmYXxLVO4M9/uRcAp2ajVWgoAdLEgUSXErsA6WvkhukzWbdqNTfecgsz/3QTna3cN0LXaaivJSP9EMkdO3D08GF6d0shxKZj1NQQYGkcjMZGqgoLCHc4cBhGM47pxeefIyosGOH3oBs+Ql0OrrrmKkzTpK6shKagRYZhkH7gIF3bdSQuJJy6skpmXD6ZxLh4aioVXwjERqC04WwEZyMEeCQuQ29+OTw6929EhYTjQudkdi5B0s51192AaZqUZ+ZglFYRLuHQ1i001NfzxBNP0qtrCk3BQEDSvXs3brnlVtLS9rFjyzYaa+pJad+exppaDu3aS5fERCoKigiWGkatmyP71CbnqWeeY9yIiwgPDGX50uW4HE7u+cscAKTfYO+OHfRMSSEsJIR3336b+Ph4/vHSy2i6TkV5Gfm5OTiAf7z4EkIIFn/+OQGmwOHHirllw2ZXTgoFuTl0SUoi3OmirLCATomJtI2LZ2j/AURGKCuOhmTNih95/923GDhoEN8vW0ZEZBhS+unXpRvD+w1A2eDVPO3eDTOvvomgoCBWrVpN797d0HQfuu5H4EXiwzAaEcKHYbiR+BA2E0OcdgbxaZK75vyFdu3a8fY7b5OZc6L5/DnolqapaxKiAKSU9ShhAWD2+Rq4QEqwvvMvoGxTmVZnOdeA8txqZv5SynSU5qirECLk3xrlv97Xvdb3bS2FKKvOQpS26zp+hyxtynUoAP68M9rZgxJizqzTG2VyWtJSiLLqVKE0iE5getNh1JL2AL8RgqSUZ42eaJnv1qG0lY9KKf/yLwpRvYH1KG/NKVLKc2q/zkaWQP840Bq4FPhJSvmO1bYG/BmlXfrrGXNnAA+grv/MufCjhLoz+yr7I2O7EPpdjZRQYMFPgXjUBL0vpfwfawK+Rqkdc4CrmnYlQoi5KFChAdxr2UF/lwy/QerevWzcuIHIyCj27F1Jbo4SaCLC+1Ba9igTJowFYN+uXQwcNZLNP6+nsrycEaNGEmCzo0lJSvfuHEpLw/D7KC8to65SRckeOGSI2l3v3M2EkaNZ/t0SAIYOHYantp76iirGjRjJ+HFjqG5o5Ok/30mA3U5cXAxHDp/CXVWhPNSE4PlnnldxhlBRiA0JefnqXVF26hR2u4POnZNZ9v33qu/Bg62gjHqTIx/1bjcH9h8gKjqald99z6nCQvKjY3hl81YqKytYu3w5DoeDgrw8CoqKCI+MZNjw4eQXFOBwumjVqg0qXpVsBsADoMGx7GyiYqK54dZbeOrRR3npicdI37eXoLBwbr/nLgpPFuPxemmT2Iqjx45RVVXJ2AnjOZR+mMSkNoSEBgMq5lQT9ezVizZtEjFN5VUpJcTFq3dpv/79LfC8YP/BQ0RFx5IQp5I6FxYUsnvPnub6o8dP4ItPF7Hgww+YOWM6piE5VXKK5T8uIzklhfDoaFq1SlBBQwWkpu1j2NBhBAYGcSg9nYH9+nLUkLRvr9zDp0ybpgDm0o9d0zGlICQsgl59erNl02bKyspJbJPIz5u3UldXz09LFvPxe+9RkJuD1+Ox0u4oioyK4qeVK7h80hVIISk7dYrgsFDSDh4kITaObsnJVFZVEx4SwvARakPepk0bNE2l7mjy7DRNkzdff4u1a9Zy9axrePbFl1i7cRMx0dEgJQ6bxvHjx0lJTiY0JJiy0lKGDR5Ig2lwPD8XlyUUC7uNiZdfjDMsFK9pNrvu9+s3QAVjFQJTgDAM4hIUj2qocyMAr9fHyZMn8Xq8JCUmUlpais/jpXfPnjQ2uHF7lObebfoxdQmO0+vHJyV+K6T8sDGjqGlsINDlZOe+vfQe2JeMQw4AAgKdlFZXMWLcGF6cpzTj+w8e4KlnnzntnCGV1vnY8UwAtu7cwbiLL6be72f7nt2IADtBoSFU1FUzfuw44qOiOHjgAJqm0S65C9htdO/bk32fH6S2sQGHdW+CQ0NI7NCeDsmdSd2XRnl5OZ06d+a5+c9jmpKsnGyqqqvp07sPuqbhcrk4cvQoXiGRSK659loefvBBBvTpw9SZMwhwuZhy5TQ8pkFASDDDR4/E5XShCdF8L666agarV6zktjvvpFvf3nikgaEJqmpqOZZ7gpFtRyBtGl4kjR7Jc88pD92Va9fSrXt3DKHjlwLT8IPQ0TUNA12B/600OVIop4gmEggCHAE8/eyz3Hj99fxt7qN88dXXp0Mv/Jb6od7VG89ybhPqvdz3nLUvnE67jv57ZTPPYR5rEr7CUULIf4L+SF9DUYx4phBi5lnqOIAYIUTUuQQVi1JQAsYWKWX1Wc5vBM7ESg21vsPE2bFqTfijrgBSyhohxI/AJJSGcQnKHLhTStlwjnHFoQTIDiivuN8IdH+APgfCgEuklGc1VV4AvYzS7sWjtJdN1AWIAjKBv4uzhwFxY92LFuN5BTgshPgate63SSlL/8WxnZcuxLTnBx6QUqZa0vpeIcRalF1xvZRyvhDiUeBR4BGhvDiuQdlJWwHrhBBdzlQ1no2ioqK44465SCkZMmQIYWGnb5hEMmbsGIKCgqivr2ffzp1kHEmnzBJeho8YQf7JEg4eOkTfgQPZvmkTy5f+QGR4KMczMoiOjaVXn94U5OVSVlhE1uF0aiuUNjqlUycSIiLp0jaJS0aNJCjAxclS9VzU19eTeewY/fv0ZsemjQDs3bOHvXv2cC5qdCsG1TWlGztjldY2OjZWecNJ9WI1JZSVK+1aWWkpSxYtAmDnzz83t7P6hx+afwc4Axk6fAQBThf79h+kTWISAU6n2oYIFe2p6V3l8XrZvXsXvXv34rJLJ4Kms/C9d/nq04VI02TB66+R1LkTD859DJvDwbHMY3Tp2R2f6Wd36m4uHTtBmRtAMWpLC9KxS2eCw0IxhcAvTaQEm10x1NCwcNB03G43J3ILmHjxBBoszFZ9QwO5+YVcM3Mmbo8XA42Ro0ezft06jh3PpnOnTnyy6FM8Hg833XobhgkhYeFUVVXj9fvIyjrBjReNtHKT6ei6nZjYOBpOFat1Ex1DXUMjgQEOPH4Dm8OOCcRaglxQUBDllVUUFBaz6adlpO/fR3RcHOMuvYT2HToiTZNAp4v33noTKU169e3FwcOHcDoVoLd160Qqy6uZNmkKGUcyCXIFEh4SSpzVfmlZOQYaSOWFqOk6mmZj4wa1Xu796wPs2ZtKbHQUAQ4bfr8fh8NBaclJrpwyGWmofIq6zUZWQT4nq6sIClEb4x2pexEhwUyLjyfQ6Wx2kAgKDcfjlwhdeSaiaTgsAHJtXT2G36C6sorUvakMGNCfQJeLrVu30qN7V8LDQqmtr2/22vNIgwZTabs0TeA3Jaam4bPCS6QePsjgwYPx1Pk5VVXB2EsmkJGeDoBfSOp9HooqTlFmeZp98tFH53w2AMoqy2k0fWzfuxu/Jmndri0/b92MqcHho0c4brdTU11NaHgY3/20jG7dupGTk0ObDkm4TT+nqiusZyKAfYcP4JcmGzeq5+Z4ZibPPfPMr/pbs/yn5t/VNdXUut24PW6uuelP6C4nn328kPfffAvTNHn9pX8weMRwxl1+GcHhYSR36kLRySIqq5RCYuvmLdhsNjp270pto5uSygrqfV5KTp7kx9Urad0+ieDgYGKiozme6aO6GsaMH0Ripw7kFhfhdnvRNTsJCfGUlpXR6G5ESklMTDRud6PymDT8uOsUL3e73WQeP058fDz9Bgyke48eLPn2W7Zt3crokRed6xaHARVni+MjpfQLIZrwIv8uFVvfbc9bSlFTnKvis5yrOssxULwHVLqH/xT9kb6iUDzyyd9pMxg4nyAVZn2XnOP8ybMcazLFTbA+5+u7ia5GaRyv5TTep1EI8S3woJTyzP7jgVBUjKYt5+njvCSEiEHx+4P/hhCFlNIUQjSZA90tTjXdi86cfy6a74WU8lVrnd+N0izeB0ghxCbgoTNB8v8u/a5pT0pZLKVMtX7XolwbWwNTOG1P/gSYav2eAnwlpfRYro/HgUEXMhi3u4GHH3kYTdPYtWsnxcU5Lc4KNm/eRH19PYGBgZQUFeGtayDz8GHCwsPp3acPxUXFlBQXc+311wOw5qcV1JRXIKWkc0oK3VJSOFVyitEjhnPxuHF06thBNW1KcrNP0LtHD2KiIrHrOrknVN8hIcFcPHYMMVFRhIaGAnDvnDl4DR8NHjde06Ta46PS4+XLFevYlZnDgq+/pfhkKULoJLVr33wFpqU7Mq0YNqHh6vnq3rMXj7/2Jicqa9iZmcML7y6g2O0lq6yCtz5bTHF9I/mFxcTFxnH46DH27EsjuVs3TGm119y2osrKKk6eLKZr12TKy8sIjozkgy+/4rk332LB4m8YMX48uZnH+fv991NcXEyDu4Gw8DA2bt5EdXUNiW3a4Ld2xIZpICwz0969qVRWVaJboRwAxcSBquoaVq5ey3ff/8COnTv5adVqlloMrKqqSrmyh4ZhSklNTQ0zZs1CSsmHH75Pg8fDwo8W4HQ6mTRtOmXlFWRnn+BIxjEys04g0IiOiqa+ro6CvHzS04/iaWxs1hLs2buXktIyy8tQUlNbT2Ojl5PF6v3kCgpi7bqfCQ10kr5/Hz369mf+Ox9yyfQZ9B0yhIlTpjD9uuvwer0YhsmRjAxCw0JJP3IEgJrqKmKjovl4wUKKi08SGhpKWWkFhw6q8w0NDRSfPEVeQRF19W6Kik6Sk5tHRYVi+OWVVRw6dAgNQUlhEVXlFZw6WUL3lK44dBt1tXUU5Rfw/Xc/8NPKVQwYMJA6y2sv50QOoWFhZGVlsf/gQeqt45nZ2aQdOMDe1H2cyM0lr6CI/IJCAA6np7N7124+XriQmqpqaqqqKS8vJ+dEDprQ2bcvjbrqGvyW1+OJ3Fzyi4o4diKbnamp7E5NJSsnG5/P8oosr6D01Ckqq6uIj4vjRPYJCosU0P9kcTGG38fGTZuotgDS8997i3Vpu9lfkENaXjbLt21m1Y5tbNyXSk5FBUNHjWTp0qUcTEujR0pXYqOjMbw+2icmUVFeTn5BPoHBQdRUVZPSJZmGunoiIyJolZBAycliIsPUcyOERmJCa/weL0ltFS8fM2ECxTVVHMjKZGf6IfZkHiW3opy0rExSMzP4bvUqqmuqqa+rp7KqkpHjx/LZ0u/YvH8fT744n8unTWXXtu28Of8l6mpq2bR1Ixg+goIDAXjutedwupw8fv8DOGQ97vpcGutP0DpB4/bbrkDIfHyeTGprD+J2q/vnCgykob4Bv9+PTdfRbRpeTyNCCAIC7DgcDgQS3abhsOvYHXZ0XT1zmqYRHByM3W4jPDyceU8rIfFvj8397Qv0NFUDkZZJ6VckVCbpaOA/EZOnySW9/wV4sDV5jZ0JNP/fTNVApZRS/M4n9wLaAaUBOhvFn6fOnN/pu9nbUErpllLOk1J2QQm316Pm6HoUsP5M2o/ShLUGNgshOvzOdZyLmpJA/rfiPDXdi+9/5160b1lJSvmplHIIShC7HIWNGwmsFkL8JzYSzfSHwOZCiHYolfBOIE5KWWwNuLjFwFoDO1pUK7COnadh9dXo8XDNNdewft06Fi/+mlEjb2XIEAgLg1On0tiyZSLjxk8gPDKSJYu/Rvd6+WXLVkaMHIlN03HX13HVzOkkxMUTGxdH9rEMIqz8WinduhEaHEJoSDAny8up87opr1abEz8m2QW5zJgxHa+U+IXEblODcthUnq+cvFw6demCpmls27ZVxT+y2ahtcKM7HFTXNHCqtIzWiW3Iy82n7FQJ7dq2YdeuXYCKzVNTU4tNaPgtUK0r0EVySleOHT1CcIATv9dPRWkp8TFxVFVUkJdXiKexkdycXIqLi/ll5x6Kiopx2AOoKK/E9PmJCA2hvq4O0/BTZzGy4uIiQgJdZGYcJSc3l/bt2mEafiorygl0Obltzr1UVVdycNcePvv0E1q3TeKLLz4np7iAyPAIykpLKa53I6WBYRqUW8kJQ8NCycrOpr6hAZ/PR0VFBZmZx9UkFxTw84YNJCUlMXr0GNokJrJsmcL9NTY20j2lK4UFBVRXVSOlJDg8krj4BD5esICENm05diyDKdNnUlpagWGYpKcfpXffvhQVFZHSuQs5x08QHhlBcqdOREdFcORwMenpSpDZtH49Y8ZOwPD5qaqopN7dQG1dLQfS0rA7HKQfOUbHjp0pKVPM/6IxYwl2OXHqJmFhYbgCAvjg3XdodLupb6gnLCwMv9+Hw26nbVI7CvLziAgOpn2bNvTs0YPysnL8Xh+bN28AIDwigpKSk81R6uvq6zAMP1OunMaYseNJiI8nPCKc4JAQFfhUQKDTwdQpk7HbbURFRnHjDdfj8XpwBgYSGRKC06E0fVMnTeLyceNxuxvRNA1ngNI6JSTE4/F6EbqO0xWAlAatW6lHMC46lPKybGZcOYa4+BiEkOhaHdfPGqWSaQvQRCUpnePZvxvaRNcTFVyIFixJiPSCZiKpweVQm8MrLw5HymMIIZkwzATzOAfC1L0c3NvO8N4l9EsJxyyPYP8uqD+5jrbhbvCbSAw6JvhBmghp4KmU9O7kpWcHP5qWg3C/RqdYP51iVQLvdpFqW9CjezA7fqmlMu8FBg2KAWlgmH6k6aemUGEe7TKbTlH/QEqDqI5+QkJ0Du7bSPahsdjtgPRjYFJcrhKRNyUML6k8/VtKA/cpE6TB2KEGY4YYmG5YsaKKE2nTGDUKaATD2if37jSHN9+ABx6Axx96nCeegBEjfq3maACqiiEhAW67DSZMvJbWMbEQo0z6miYwDJPYCCUQmlZiZcMwsdl0kHDCUMokh8NBXFwsNl0nMDCQyVOmMGnyFH5ctpQlS5ac6426D+UFNRKFW2lJI1Fal9Qzjpv8Qc2PlDJbCLEB5Tn1EAoQ/BsSQsRxGpP1/h/p4w+SgTK3/adoB3C5EKK7lPLwv9HOUdSy6COECDuLeW/0OfoGuAgVYuIPkVRhJz4XQnxp9T/ibCZIKeVnQohGlBfcZiHEWCnlsT/YXSUwlwvDyv0rdBT1iA0RQtillL/BPZ2PLEzZCmCFhbe6BXVfmx6gZuD6hVjOzkYXLEgJIYKtju+z7LHnLHqWY7+xi1vuibcDtGmjBPXQEC9rVs9mxgzJkKEzePKJpfzUrJU/RWLbtlw0egyBQUEsWfw1H733HpUVFSSndKW6qpqO7dpz6mQJ1ZXV9O7bl80bN1JRpswNXZK7cujAQaSUlFdXsWr9OjItzEZ2Tg7RrVtRWVtDYepe7DYbYdYOtKioiAUffkCr+HiGDBzIhIkTWb1yJX+dM4e/PfkkRSdPUu/2UOv2kJ+bS1nJSeJioomMjKSuqgqHpt5NFZYWQkdQX1uH0DQiIiP40y238NjDD/HjV59x0ZDBJLdvT2igi9rKGkIDXQToOrt37GTo4IEYEuLjYjmekUGQy2VF9FSBCmnK5wW0a9OG9m0TKa8oJ8LlZPjgQXh9XlpfOwuv10t8q1YsCFUbyM4dOjLhkolUNNSza/8+HHY7UWFhmM5A0CQ+v58gl7oXyV260KF9e5wBToSAqMhIyk8qk3PnTp2478GH1E5aCFyBLsKCAnlz/gskxMfTtVNHPF43rWLD6ZGShN2mkXn7n3j+6fm8Mv9ZAG6/8xpSkoNI7jwEISRC+EhqFYEkDE0YCEqJu7gVQhgM6tGJogyFZ9yzYzvSs4vo8HZEhhiYGDz1xMfU1tYw8+qLmH1jLM4AN/v3K6tC9pG1PHhfIgLFnMvLati+Xm3YXE4/w/qlIaVJh1Y+ctNb8cbrOXz5yaO88urFuGu/xeUwKC6rZvFXCv+mi4OEuV5CSoOGKgNdGmgY9OtTj9vtxduwnSAnmG6DugaDWn7NyMuLTv9urDCozDfw1ClHk+qSx8lIfRbZdL5emXwO749RbXD6uS+0Qt+59NdJiIDyYvU5J1lVd26egfcsCBS3leLw+JEpvzlXZhkJyk99TH72xwCMGwUffwBvvPY9CTHf07Xrr+uYJhw4AH36nGdMFo0bCzt+gX++8jOvvgqWXElNDXygfBLw+yupqTotJ0ydCosWGbw4P5W774aAM0ItlZdDbS20a6f+37UL+vcH/QzxwXIGxelsOqLT9IBpWjAdOui8+SbMmVPLvHkmTzwRwYQJIYgWicZBp1WCYMIEgz49e2GTQmUTEH4wFPZQSj9IsFkxqnRhgmEgraC9oGAANt12OhAwkvkvvcjKFT8xd+45tVIfoQSpF4QQo5swMkJFip5vlVlwRp1yIPFcDZ6H5qCY/iNCpap5p+VJIURrVEDOSOBjKeV/UyNVDvQSQriklO7fLf379BpKk/GBEGKGVDGSmkmoFCk9pZQ7zlrbIqnc9z9HRfKeRwsvPyHEAM4CWJdS7hFCbAGmCSFukVL+xl4uhOgJlEgpT1nmtQ5Syp1nFAsCQlCmy7OmbJFSfmuZ1L4BNgkhxv9BwbEBNcd/SMC5ULLM0W+gwOivCyHuP3N+hRAJQITlOIAQ4hJgnZTSf0ZzTQqflrixJuGyLXDOAKLnowsSpCwV8RLgcynld9bhEiFEgqWNSkDF1wClgWr5QLYBfrUAAaSU72PtTvr0SZZQgt1eTlTUV3z5JXz4IUybBldeCZGRUFY+kAUfhTDv748x+7bbEUKQfVxpQ6ZOmUpCbAxXTpmM3zSwOxxcPmkSa1etoqiwkM5dunD77bPRNQ2h6wwbPhSf309DZSULgaS2iVx+5ZVER0Zgmn4QJsLaEYaHBTFhdD/atkkAYfD+By8wY9pJ3n/vfdavW82gIX2IjYumqOgk6YczOXzwGO9+8DRjRiQhTAPhe965yAAAIABJREFUV1rP6IgGYiMzEKYkOlztkE3T4IbrWnH08Dg+/WQ9l40dwshR3WndOoLKylry88vYtSuL6TMGcvGYKUgk+QV59OzsIUCeQPgNasvVTl9gINQawlvzP7RqHUhUoMGfpi/iiYf/TL9+MSQkuJBIUlPLOHyokm7dwuiW/Akl+QsxMGgdWEpgoJOC4zYrXLuJlAZlJUpYqjj1AsU5b54WAjAozlfrsfzU65w4+r5i+JagUFyk1nBNxZfs3/nlb9ZV/95gt8PJ4hI6dIBA23SOnhn15Tzksx6lgQP9TLniPkaPhqgoOHhQfeLjYdbVWygrUub/8CDo0QNWr0xj8qX30qOHYpq7dkFiIkRHg2nUUJz3bHMfl18GK1fAip+OkXnsGAMGQH09bNwIvXrB1q3g9WRTdir7N+Ob+wjs3w+vvXZhwkNLsqyWGEYNvhavp+Z8c7+BvzRJAwaaFoCuO5UjgrCYO02/tebfw4a5+frrfF591cHYsREEBtoIDbFz9TWtEULHZjsAVBERMRHxqzZ0QoLzgN2EhAwhLrYLAo2EeBtvvFHMn/+8hnvu8TF0aCKdO0ejaRpFRXWkpRVSWekm4+gTNCUHbxI6VNtWUm9Np3NHwe5d77JmbRp33BHLxIkD8PtNli/fSd8+nSgq2k1QUE96dn8Z0NCEzssvSspKn2TZsq3s2R3DyIsG0qpVPKVllWRl5bFj5z6efOJ+Zk67C4TO1Km9CAgIYPiwwSS1bYuUsG37DvbsTaVfv37ce88WApxOkBAeNhbYxEXDajCsRN+DBxUyfsI4nn46l8TWr/CnG29szoljmiabNm/iqqvGM2rkHDas+1klIRLCEoA1kFZ+SmtSVcpCA86ILSit9D1CCAxT0qVLF2bfdhvvvvMrmaXFGpFfCCGmoFJ0HBZC/IAa2VSgPbD4LMDi9cA1FmB5L4rxbpZSbj7fOpVSHhRCXIEyHb0thLgHlYC3FuiEEkQCUeDfu87X1n+A1gMDUTGWNqM82PZLKX/8VxqTUq638L8vAJlCiBUoRhuMCkswCmU6u+QCmnuM07GSBnA6jtTVKG3J5LPUuRb4GVgghLgXZQmqQvHUXkAPFCj9FJYlSAhxBKVtzEfhn65AmQ5ft6A557rWH6018z2w0RKmLjQOVGsU5CcX5Xz236BnUKEr7gQmCSF+BgpRglFnVFiNvwHpVvmvUPiwrShnOIHSQg1Ere+WXoXrUaltvrPm2A3kSikXXejgLsRrT6B2L0eklK+2OLUMZV+db30vbXH8CyHEqyiweWdg1/n7sNOq9X1IKdm5s4D331/ChAkdefqZy/B686isWEq7pCyee7YXV0518NGC90lKcpGT4yY83EaA/W4OHVCAXdPapbeKOR1LrGePQo7s64jk9M5fYlCUpfT1xSfupiD9LgrOMjbTv5+KkqFUtIDpPfcsLF8O69fnsuyHXLxeiIiA1q3hnnsgPuYJjh5QZfMs+ba89AuOpn9x1uu/+SZISYZly+rZvGkXdXUQEgJxcXD1VTBhwg6KctWmxwbYBJw8ixK1Kdt8eenn2C2+Ons27N7t49ChfDZvVjv7uDi4/XaYMqWaurrTDpUODfyNUHdGGDYrFy8+bzGN7l+rOCyYDabZgN9/LueQJtJ/xdxjYnSGDGlgyxYfU6aG4HCE/Irp/0YIQAOhowkbCA1HQAGQx80392PiRC9ffJHLxo31BAbamDIlnnvn9CQ2NqSZQQtN54MP/Lz+eiqbNuXz/fcNxMUFcfXVKdx5z1CuuPRjQNCm7RyExZyF0FnynZfXXl3HihUHWLKknsTEaObMGcGgwclsveJ52rS5mJSUGy1thN4sdAQF/R04RPv2L9KjVz8QtnNfmyVU6AgEOjHRjwLf0bHDKwwdMqtZ4AgLmwpsY8RFDbQURoSEE9kLgVvo3Okdho+46Te6YdHiL8CgQRKf71U+/PBDvvoqG6/XS1JSEvPnK8EzOHgMsImePVf+pqFduxcCu0lodTvJXW+y0i0JunSBUaNO8PLLL7NmzRr27k3H4XCQ0KoV4yZMZdq0K2nTbqoSDLCiNABYaYVU6iETXdf54Ycbmf/ifBYtWsTHH68jISGBm2++g7///e8KN2SLJDxiApaogRDww7KNfPbZIj5btIjVa3+hrq6OmJgY2rVrx1NPPc2s667DEdQWKQTPPPcC69asYV/aAVatXo/T6aRt2ySef/4Fbr1lNg6nsxkL2OQv6RcSU0g0XaNNUiIbNm5iwoTxzL59No0+L7feNhukiYHEsJ5BKcBjeaQLEzTdYXnl6ZiG2ZwSSAitOYK6bOFJKq00ThKsJOXw+JPz+OLzz6mpOScsZRbKU+kWVEwdUMzuFeBsEtgclLA1DrgMhZ99CjivIKWmTm4QQnThdNLiG1Gu+aUoIeGDfweE/AfoWZTX3SQUY9VR+N1/SZACkFK+KITYhgIsj0BhgKtRTPx9lEnsQtopEyqq+/PW+AagAkvehWL0vxGkpJQFQoj+qPs6HaW50lHg9HRUIMqm/HQ5KCD2aJSpNRoVRDQD5Qj2qxAK5xjjaqEiiv8IbBBCTJRS7r6Q6/tvk6XVm4rCe92EWmfBqDV2AqWtark5eBQVd6ofaj03ogS9R4B3zjAPfogSjK9BBQe1oZ6dCxakhPx1RP/fFhBiBArRf5DT8SkeQ0nHi1HqsDxgppSywqrzN9QD7EeZAlee2W5L6te/v9y27RdAMvfRR/mf//knr772GnfeeRd1des5fOjy5rJPPAFbtsC8eSj8wn+czs3ETx/TEMKmGJjQFYMUOmqN6xYD1ppV/U0MVhOKTUKLujRpCWzNzL55DJqOx+PHHuBECBu1dW7yC4romtIdTdOwaXY0zWaNR42rSdjQNZvKm9c0bjQ0zabGg47HEFSVVZIQE4/QbZRV15B64CAjLxpNoC0AHRuyuR1VV0pN/S9V7jxds3LsmRKh2Zrvj67bMU2VwwzNhjTB1pQ/UcVpAKHSzyR37kZJSQn5hTmEhUYgsPwPhUBaOdqEEM0mTKmdTk4rTcVkNE3dV4m1uVeRDpsxS9JsSvEhLY2BVCvTtCE1CbrAAHxSJTpWs44VLFH+SiA5nUQMPvjgQ+68/U7eeedt7rjj9vOuKolA/q5vh8lp56GmbsSvBSKJWouceaxJILEOWcmuzzsmS3Bp6hlh3fsmIcc6ZwBn7bDFv00BJCUq56JJUyJliSFNlbvRSgBumoZK0mtKAp0u/F4vPq8PTRPY7HYafV58loTusNuRUtLo8SClxOVyEeAIUFg7U6LbdJwuJw1uNz6fH92m43K6aPQ0Ynp8ICEoOAiv3099Qz3oGoGBQfgMP9U11ThtdoKcLjRNo7auDlNKnAEBOJ1Oaqqr8RsGAQEBOAMDqa2txW/4CXA6CXS6qKqpRlo5L8NCQykvL0dYSddjoqKVF6MQaJog0BWIu7ER05CAjZiYSMrLK/H5fPj9BrExUVTX1CKlxNvoJiE2ivr6empranEEBBAZEUFlVRWmaWKaJq0SEoiOCEfTtL1SygHnnej/o/+j/6P/Kv2uRkqqZIHneiOPO0ed54DnLnQQmhDYbTpCE/j9SlCsKC/HZtMJCe5P9+7LMCwm7fE9CByiY5fn6dF7pJVkV0NoKvyf1sKEoQnrmLBhSmExeLBrDpUcVrOh63aQVn2hWUxXw5Smyuyu6RbvtXaFpgkINE1DSsP6BoTANCWmaWK36fj91jnAFNKKj6h2ntIETVdJg6WVpFkikNJs9pAzpMQwTPYfPEygPYiIiDDS0n6hd6+exHdshw0VEVkgkIYCx5tNCYyFMiFYA0OioktLK96UIcGsqyPrwC7adhqO0HV2b11J26SrCI/uBVIgEGg0XZ81TgRCO51Qt2knrUnTSlishB6lEzTUDluCX5r4AJvQMAxlFrHZbHyz+FtOnDjBLbfNxh4YQqMhMQwDKUHXNaQUGKZihlJo6LqOx+PDlCr5s9MRgNvdaAWp1LDZVDphr8+L1/h/7Z13nBzVle+/t1LnMFmjUdYogISEBAYJAQZJJBME2F7ABrM4sOzz+hmvve/Z3rdvd20vn2dj7/o5LCx+a8x6MUIOBGNsK4IRkpCQkFBOozBKkzQzPaFDhfv+uFU9o7BgMKBAfT+flrqrp7vr3q6ue+qc3znHJhqJIKVUC6ymoWsqY7Jk2xiahqEZSM+j5KgmzkIIIpZJsVgKDmSSqSRNu3ZTXVODYepYpknJdmlu3sfX/vHrGIbBRZdeRnNrB57nkUzEsW2bfKGAYRjE43FKxRJ9vX2YhoFAkEwlaW/vQAjQdZ2ammpaW9v8RsGS6ppqDh08hOuq65a62iF0dnZSLJbQdYNkMkF/vp+uzi5M0yCVTKAbBm2tLQggm6kgGk/S1t6O5znoumDkyBHs2bsHTRgU8kUaGobR1taK5znYtkNdXS0l26Yn14PjOFRWVmKZJm3tbUggHouTyaQ5fKQF6XmYpkFFRQXduRzFYhHpeowYMZz+fJ6jRzuwHZdsNkssFmN/834syyISjdDQMJTm5mak52HbNo2NjXQe7aSrqwtNE1RXVYH06OhQsoXamlpsx6arqwspJZlMliFDhrB/j3pP07KorKwgl8vR19dPJGJBRQX5fIF8fy9SejhuCSmhu7tLFeCUylJ083n6KKAJDcMwcF0X13XR4jFVD0z3i5IK0HWBJiS6JrAMHcPQSEQjOK6LoeuYukY8GlG/ESEwdY1MMuHXndOwDAPdDxMamkFM16lMqmNFIklYJkZa/b2MW0QjFromiEUiaLpGLBZBE1lV80xANHJCQl5ISMgp4k09Uu8FF154oXzVr8u0YMECbrvtNurq6li7di0NDSrhryQlz/3meT5y041EIhG27tlHdVWVLzGQaJqGY9uYuoZ/HY8nldHjeSpUEIzV81x0Xcd1Pd9VrjwXSJVB4/nGjebXUBL+VaWmaerEJyW6piOEUHVffDPTNE1s21F1ojwPQ9dxPA9Hgut6RCPq+ZKtTuwRy8IwNHK5HlxPEo1FiUWjdHR1IaXy2NiOx3O/eZ629g6mTJ3GZbNmUigUKBaL6EAiHkF6Ln29vQggmUphOw6lko1tOySTCSwrwqHDh9EEWJEolRVZdu7exeKlyxgzejRSwt69e5gzZza6blBbXU1vXx+lQi+6ppHJZCiVSvT09KDpOtlMFik9unM5XNclmUhgmiZ9/SrF2zItamtraG1rI5/PownB0Pqh9PX10dfXy388+ih2yWbBE0/gOA4/XfAk48aNx7SitLS0omkasVicTCbD4SMtOK5LLBajpqaG5oN7cVxl4E2cMIGm3U0U86q+1LChDSDhwOEDuJ5LdU01ruvS3dWNbhgkkyl0TaenpwfpuWRTaSzLor1deQ6SiQSJRIJcLlc+ZkaOHM7cKz5IqVji3EmTqKqqprl5Py8sXUqhkOdvvvwVPnb33TiOg65rpFMpbMelr0/1gkwnk3iepLe3B9Mwy9lXuZ4edF1H0zQyqTQ9vb3ohobr90/s6sphmspzmUwkyOeLuJ6HrmtohoEnPRzb9tvrRNB0nUJ/Hl3XsCwLTVcGoWEIHNchlUyQLxTQNR3bVo8LBZvASRiNWsp75LhIKbEsE00IiiUb0zRwHBfTMPCQ6JqG4yjxdZCFBhLbsdE0zf8d2Mq7KMEwDTzPw3FcLP++YRjlOQ68LEII9Xt0Hf8CRvoZbcqYVw5FiaEbygsqRPnzHcdT2YjagC/N83Wm6rfsXxAgyx5NpLpgCS4Agnpp2nF97MqVJKUK8GlCK3tOB3Oiv1Ge5G/EwOf/F5z4qpMj1L6EHqmQkFPMaWdIeZ7HNddcw+LFi0mlUtxyyy0MGTKE9RteZ9HC3yOl5Kt/97/59L330draipSSSCTCqFEj2bZ1K8Hpra6ujlwuR39/P6ZpUlNTQ3d3N/l8HiEEtbW19Pb2ks/nsW2boUOHUiwW6ezsRPcXO9M06O7OIYQgk8lQU1NDS0sL/f39RCyLVDpFV1c3xWIB0zCpqa2hra2dQkGlqmezWWzbprevHwlk0mlc16G3tw+hCdKpNLqu09nViRCQrcgSjyc53NKCYZi4nkd1TQ0Hmg9QsktkslVkMxl6enqRnoOha1RkMniuMqSk9Mhks8qQKhZxXI9UKoVlWeRy3SpMgiCZTNDT349Asn9/M4lEnJqa2vICEo/FcFwX4Tm4rksiHlchh1IJoWlEIhGQkmKppBZOw1CeH8fxK55LTP8KX0qJ4zjEolFV90lK0vE4pmky8Zxz+MYDD3DF7Dm+saqVaxsJXVV9dtyBUJ6uqeKTgR5GQ3nhpCcxdB1NE+hC4PjhJJXmr6mxCA3d9xAqQ1jgOeq9DN876HmybDSU11oJj/zbwzzx+OPs3rWT7u5ukskkU8+fxl9+9rPcfMutKvyHxJPKyFAGhV4+nkF5npCq2rhpGoDA0JXexfNc3yDQkLgDBoLwn3f9sJgmfEGyWvw1Aa6rPJOIYJGmbBAocbLne+Jc5TV1VSjUdVElPqQa42Avo/Cbegfe0sCACQTPgdfT81QpB1XNXemGQF00lLU+vidT+u9t207ZsArmu/xZ4Ed9pRojIAa9D6g5EoiyIVXej2BuZHBhFXhgBZ70yhdbgx8H3mbPv4AKLp6CM6IW7BCy/F6IgTEdT/nP34TjX2vbnUjpYBhpNC0SGlIhIWcYp50hBWDbNj/84Q+ZP38+W7Zsob+/n4rKSqZNm85n7ruPuXNV5e1i0UY3lGcoErGUAeOfzSzTLOss1BW2hes6foKMMr6CgoNSSkxfiyGQfsVngWkYvnEQZBcNnNClH17ypFe+ehZCK9eECfQbxVJJLZxBGE56fjjAKF/VSulhahq26/qvM/GkVFXF/QUjaIKrCZVGbuj4eguJ57lYhnnMggz+YidViQRR1gopY8J2VLirPB44JowXePAC75rrexGCcJ6a5ROvyoMwqCeVcWPbNqZhoMmBVwRjCt4gMARcfxEMQqC6rvkaKBWSQ0qkGFjUBAKkp9pruA4IgaHpOH42VNkr6LqAVF4JBtY6zV95g0VUaNqAxsifB88XPgeLqOu6agHT9PJjTahEK+nruDx//ga3ntF0M2i/Vv584XtPXFd5mgQe0rNV+Nk/BpCBoaCV5wUhy8dZ+TkCAyAwHnzD03P9/22E0HBdWda5aZoyXpQ3TS8f10q/NzAPytBRc+Q4rioYqWnHGlvI8muDQQoot1QSmoZdcpSmz39zTRvwKAn/uFbzrXRGCHVMDvZKCSH870ArG1KDj5nAqAqORill2RgLvufBKO+SKB9nBBFxysM47s7gA/24TScxok5qVx23D3ua/hf79/+T/5SJrqfRjRSGnkbXU+hGGkNPoRsp9VhPk62YTUX28tCQCgk5DXhLBTnfK0zT5P777+f+++8vb7M9WfYmBIt13PeU4G/PpFK4nqeEsprAtKzyydp1XSzLLJ+4pSfRIzrBZWzwnhKJ7veRw/d2IYPVXoUy1EKlrl51TWXeqIVTovlhBNuzEZ7wtT4OmtDxPOWRUXVjXFxlHahF2gNL0xC6wPE8DD87R0oPgaZ63nkuhqErgbSUmJrws5xUM1WJVFoz/IrkUnWUV3HOQKOlDAFLA/CU3kjTVMjHsdGF5htdLmg6TnmRV1L2wFMAgfElBi2myjuhFidP7aNhgCfLC6znKW9QsPh6fsiGwGuhaX5YViKkEntrUqr9ASXYloE+TCJ8z5MufM+MtBFSoAsDx5NouspyFEKZGsF4Xdfzs8+VZ8h13bKOLBidEBzjWdN1fZDXxSlrYVQ5CD+041tVnuehB3o130D3yl4R3yD3hfLBe0op1fcuJKC+P+l7uQLXkSbUl6lrIFHfjSc9X/2mjnWh+XJ0KdGQeK6D5n/fpq75BqaG53p4eJimjucqj5UeGBN+uE0dGyClMoaUTSJxHRtN10F6vhESGOWBxvDYMJl0A2NfDnh9pEQXWtnDJvDrKzHgbVJTqo4/13URmlBGvif9KgHq/QLtkO4X0hUMhPgCw3vwPAcWjpDCLxHl6wldOWDoSP84FMFpYsDT59855tj/I5xRJ6DON+mBx9LGcTpwnA6Kb/Q6aVORufxtfGJISMg7zWlpSJ0MX8JU7jUWaJ/wtURqQZE0NTVhmSbDhw9XYSVvYHEOQg6uG3ghGDB+9IErX+V9UEvq1q1bqa2tpbKiAs+/Ki/Ztr8A62hCo629jR3bdzBjxgwlWvU1WIEo3fNcpOfhODZdnV1seH0DV1xxBQC6NlANMNCKeFIV6xO6oRYaVHjH1EB4rr/w4NekUSf3IE3bcYISD7K8iLmO2h/HUc2Gj1mkdL1s1BiDKhMKTQnWDUNt03Vx0tCFmqsB35QQglLJ9j/PplgqsWL5y1x04QfIZDJlz4rrqjparh+20nSdkusL9W0HXVNGqus6SD+M5XkuhYLNksVLmDvnSmzbJptJK+2KVKEipIemR33BuoFjO4BE01QKuzoObF/zE/G1O045rBjod5QnYyDUGHioXNelUCgQi8XU3LquMvY0TRkjDOiCghkCZS4b+qBsO9+YGAgkKVzP93S6HkIoT+eunTvI5wtMmjQJFRIMDGA1x0IYKsXeD71quo5wPTzXxTA0FXr0Q4QSj9c3bCIaSzCucSyea2P5FwuBAYv//eAbCUEmmo4oG0xSCGzbxtB1/7v0vZeuB4ZW9pR5nuuH8Ez/GJe4ru0bM6I814Gn05MST3i+DkqF4AxDzWXwf3B8HnN+0E5UKB1zjPrfoe3Y5dCc67l0dXRRWVGhvv/iwO8DKdF1ZUQbpvpNF4tFTNM8Rj8ZeD0BNENncNjujwrRSRCa6r5QW3cPmez/5rvffZD21ja2bdvJpk076O3tZ968Gdx733SqK7fS37+s/LsHhBDi88D5qK4T5wIm8Bkp5f978x0ICQn5UzljDKnyRWCwoRySkuWrbE3X2L59G/39eSXGdRzGjhlb9mIJVN+3ttY2Ro4cCULwzW9+k8suu4yZM2cSiVi+98rxw0CSp59+mtGjR/HRj36Uja9vxLIsJp4z0d8HKBZLvPzyy6xZs4ZLL7sUx1aLsivVCVZ6ElwBGhgYNO3aw7LFy7jy8tnYpRLC9MMThgptIHS6u7o4eOgQ+5sPMGXq+Rzt7GTrlq3U11Zw+WWXUiyVKJZs2ts7KJRK2LZqP7P/wAGuvWoO2XQSXdMpFks07Wmio72DKVOmkEql/DCSPij8MXiOB28bfGV/3Nyf8MLBf6/a6khAaDq5/jxPPPEE5557LrFEQoUxTRNN0yiUbDZv3kxlZRXduRwiGufIkSOUCnm2b9nM5z7734hGLEDiODaGYaDrOouXLuPw4SPU1w9h7pwriUYtBEpU7dg2+5ubkVLQ39+Hpglee+01Zs2awchRI7AsJeDWdY3W9qM0NTXR2trKhg0bSKVSfOrTnyLmJxJ4rgrbFktFNKGxaetm9uzZw0t/eIk7PnYH500+T3mtdJ3Wllb27N1LLBbjnHPOQZWwGDRHysV54rQdhyeVUWzoBiVHeTJ/v2gJVZWVNI6fgOu4JBNKCO66Hkda2tizdx+N48azfVcTQhO4nmD5smVcfdUcpp5/nipEK6Xy0Gk6nd05fjv/F0ydMpmrr5pLOi1U4cnyvoIQOr9fuJARI4ar8fhZs8oDqkLftl1ix45dtLW3s3PHLpqa9nDnnXfS2DgGy7JUA2dNQ9M1HFdy5MgR9jfvZ/lLy5FS8sUvfrH8w7Zdh507d9Hf38f4CeOJRqOYponruWUvYtkgIvDkvhkDXqkATWi8suYVNm/ezLRp0/iPx37KeZPP41Of/hSmoZfPK47rsmHjRiZNmkSpYHPk8CF+8pOf8KW/+RtSySQSJcxvPtCMJjR1PpHHftzJdFQnM60MXbWKKRY6WL58NT/8/qPk83mSySQNw4azfdt2urqjfO97TfztV8ej6l0ODAn4rn+/BVVj6O1UKA8JCXmbvGnT4tMJMeimPAQenqeEwaVikWeeeVZlC/lXt7t3N/l6pwHx6coVK3nhhReQUtLZ2Ul3dzcTJ05A1zVKJRtQIZySXaKlpRUhBD25HlavXkN7RwdNe/bgOA6uo7wPBw40s3DhIq699jqeffZZ7v/C/Tz9jKpNWiyVlI7EE9gllwMHDrHy5VdwbMm3H/wXHnjgmzQ17cUwLLp7+jh0pIXtO3axe+8+fvGrZ1j24kssWfYiv/nt73nt9Y1EolEc1yMSibJi5Soef+JJVq9Zx+PznyRdUclzzz1H88FDGKaFZpi89PIKNry+ibaOozz8yI+QQiB0Aw+1UL7RDT8z6dg5P/F2wvfie3ik53Ho0GFefXUtvX19/PxXv+TrD3yD3y1ehIvEkR5WLErJddizfy+bt21jw6at/OzJX+BIQdvRLr8Zs4YKjAkOHjxEc/NBMplqauqGsmnzNnbt3qt0N5qBphscPHyIH/zgITo6cnz/+w+jGxG2bNvGCy++hGVFcT2VqeUhaG0/yopVa3jqmV8z95pruWHezehmBDQDR2p0dfdypLWdXU376OjqZsPGrTSOn0gqW0lPbz+e0NFNi+5cD/MX/IJEIs38+QvYsXM3yteklW+Dg03/1U0CUjNxpaDkSnp682zetp1XVr/KwZY2Hvg/D/Lwj/4f+f48tuPhemoch460sqNpLyteWcNvFy3jUEsbbUe7KDo2aDquFEjNoGC77N63n75CieGjRtPTX+B3i5agWxEcDxwpkJqOJzQcqXG0K8fOpr1qGwJPCByEej+hkS857G0+yD9+4wGqamq54+MfY+jw4RiRCCVX4gmN1o5Odu/dz/6Dh+no7KSjs5NJU6ZQsEt4AlxUgctFS5eya08T23ft4ue/+DmmZaomv4bB8bqmP/UkUlVVxf79+ykWi4yfOIHLrvggaIKS4yB0HRdo7+zkoUf+jf7FlwCPAAAanUlEQVRiHsMyWbR0KdM/cCGulNieh+26CF3nxZdeYvnKFciTXJj8sRhG0IS5n+uuvY65V1/Fb3/3O9o62vnBD34IwND6eiZPmoz0m4QP8kh5qIKDQ6WUQ1DtYUJCQt5DzhiP1PEIIcoCWddxWbtuLcuWLeO2225nwYIFFIsFxo8fj6Erz0CpZNPX38e27duZMGEC7e3tLFy4kEQiwXPP/YauzqPc95d/iWUp130+X2D58uU0NzdjWRbLX3qJUaNHMe+meQihYZkmxWKRvXv3cccdd1BXV0djYyNHDh+hpaVFpWTbnso+Mwy6u3tZteoVVq9Zw2c+8xk6OzuZN+9GArHxpi3bWPHyCnY3NXHjjTfhuC5Dhg4jW1FJ29EuJk8ZxfQLLlQCbtdl+IiRbNi0hVxfHwcOHKY3nyeVqWTCxIk4rsT1SljRGEOGNhCNRnl55SpcXzOlDwrhvRvfS5Cpt3nzZh566CE+/om76Mn3Me/mm1XIFRUSc1yHYSNGkE6nOXj4CL9btpJILEH70W5uvvVW0FW6ved5CASLlixl267DjB49hkxFNW3tR8kXC3hCA+liGBb1QxuobxhGT18/Bw+30t2Tp1hy+NANN+ICQjeR0qFol6ipq6PkONx0y60gdLZu38Gvnn6WWZfM5MILL2DJshdobWtl7atrueeT99De0UFldS3ReJxhI0epcKqrYUZiZDKVjBg5EtOK0d3dcxLPg1S6sTeePKW901U9rNa2dh5/4kmqauoYO24C6VSaSy+dgYWD47p4EkwrysUzL8GIRHlt4zZaj+bo6MwxecpUpl9wIa4HhqHKcOR6+/nWg99h1iWXUV1TR6GQp72jXemANFXiw3aVtk3TNCKxGLbjsnrNWnI9PcyccTHxRLycrGBFIiSSSS677IOMGjOWzu4+Xn36WXRd48Mfvpmurhzf//5D1A+p5UhLK39224c52tXFsGENzJg5k5Jt+1l7Hsl0Csu0iMairFi+jXIh0iBT8ATeZC6Bk/XglVIydsxYGhsbcRyHdCZNpiJLyXEwLBNHejiey+ubNnLp5ZexY+cukqkkK19ZxVVXXcXf/+Pf84m7PsHk887Ddl0yFVkikShF28YwIyfZhz9iL3WlkXLdHJquUVNTw+jRo9VvqZxCgP8daSipZfkIk29W8DgkJOTd5YzxSB1/KhUIhFQao85OVdBv37597Ni5m5aWNqqraymVHGzHwXZchKbxve/9kOrqal78w4v88ulfce7kSXzpS1/k2uuuYcfOnSr7zlMLSTKV4kM33MD506aRSqWxHZfu7hwrVq5U6eSuR6FYYNUrr+C6Lt968EFc1yVfLDBp8iSEpmFaFq6/SEXjcVra2vjsX/0V5009j2UvvEhXrkelXiOpqakFoTFlylS6uruprKqmWCiQiEfIpBN4rlrQVAVqnZEjR1Eq2UQsi2QqxfO/eZ7Zs2f7YTOBqZvMmjmTWZfM5A8vvsidH/84hq8VE4F69g1ug8W65TkXJ/OkKL+R708BlJZF03V00+CDV17JTfNu4kDzQQ4dPkJffx7pSXp6e3nyyZ/z11/4a3bv2sWQuiFc/IEPEI/FqMik2Lt3v6onJJQQ3pPwoQ99iHg8jtA0unM9XH7FFUydOs3X3XhIJLFojFQ6hTA80tkUGze9TiqTpaKy0g/bSrq6e/na1x7g5RUr2btvPwLBf/z0P5k4YSK5XDdLly7Dkx7jJoyjvz/PmLFjqaqsZH9zMzu2b+PQocMsWbKUf/rGAxTyeaLRCLfd/mfsbtpDPBHnvPMmn+QAViL0N7oFxRwDfVp1TTWWZfHXX/oC8XiEV9euJdfTi+1KEAYvr3yFb33r2zz80MOkkgmuvOJyqrIZKjIJYtEIe/bsK4v8NTRqqquZcfFMJKocg67r3H7HHX6pBr8Fia6hG6rGGkKw7rXX2LhpE5FIBNMyy0Vnf/HLp1m0eAlLl73AmLFj+dVTz9Db18v0Cy7gpz99nK6uHLFYnJraGgwrwo033sTOHbso5Ats2riJHTt28LWvfZ3Vq1ejaTqXzJjJ+VOnsm7tOu78+J3YdgnLMAeJ9Y89F7y5f08J7x3HKScLuK7L4cOH2bR5E/X19WzYsAG7WOK5Xz9L19EOpOsiJKx/bT1r1qwlnc7w/R/8K4cPt/DVr3yFG66/XoXzCwUEkqhlkU1nSSYSWIbx9tTmDBhSttOF57jEojEcX16giSADF38MyoB8I01YSEjIe8sZ82tUWWMDtyC8VyoWsUyTikyWxrHjuGTWpXzuv3+eRYuXsGLlK0p8LZT4dWhDA5phMnHyOUyaMpmCXWDh4oUk4gmymYzSz0jJju3b+O73vsfP5s/nDy+9xKHDh5l16aVUV9fy0EMPk+8rqKwhoVEoFujuyXHBBdPZt38fK1etYtSoUUqA66fOuxI2bNzE6lfXMqShnl//5re8vmkjP/7JY2zfuQPDNKgfOoSRo4Yz7+Ybyef7ufqqudilApXZCuIRC1ybRx75ERKVgh6Jxrjj9tvJptNUZ9Nc8oEPcM3cK9E8iWfbCOli6hqHmpuZPnUKUyadi5Ceyhh0nTdfhoQS+J9w0469CREE3gYMKYnkcMsRHn3sMS6acTFLl77IulfX8Ysnf8HLf3gZDUgn01w15ypuuP4msplKlv9hOf/56L+zbtUKejqPcuHUyVi64WeeCfp7eunN9XDFlZfx+sb15PO9OK7DI488wsvLl2NoKptP13RuueVGKisrSKfjjBzVwKc//Uksy0KiITSdRCJFPJ5i6nnnMaR2CLU1tTSOGc3wYQ1kUinOPWciSMmo4cMY3jCMP/vIR6iprmLMqNEIBDfdcCPTpp7P5o0byff1g5+h13W0nU/c+TFSyQQCgoY9/s3v9/cGN9AolpS4PZ8v8O///igVFVl6enpYsngJixb9jsd/9jM6u3tA15k2bTpz5l7FDTfcwGtrXuWFhb/nl/MfZ8OaV5g4fixD6+vQNfAcj/7+PPv2NjN92vkYGmzdsomRI4bx/PO/YcHPf45tF8sLtRLaSyIRk+uv/xB//ud3c9mll5SF1kLTsCyD4cOGc+655xCNRhkzZgTDh9eTzaaYMuVcstkMhqnR2DiKiy6azjnnjCWVjFNfW8cF509n1oyZWLrOlo2bVVam67G3aS9zrpzNuMZGLNMqi7p1Xx81cHyKN59LqeF6btm48TyPXE+OLVu2sGzZMioqKti0aROTzpnITdddy5CaGixNVTKPR2PomsHQ+mGMbRzP2MZGFsyfj/AktdXVRCMRdL/eRcPQehrq67H9FjZv9sM62cWIZVYA4Dq5skDf0FWwYHBWsq7rZdH+OxruDAkJ+ZM4Y0N7oE6Ovb29LFy4sKx5MA2Nnp4cq1ev5pF/+zelsfBVoPVDhpDJpln3+jri8TjnnTuZrs5OWltbicaimIaJ6zo0NDRwzz1/TjwRJ2qZpFIpGobW89IfXuDiD1yIaRoYhrJBK7MVjB0zhmuvvpr9zc3cfdddDKkbQl9vHy0tLYwaNYrdu/ewefNmRo0cQSwaZeKE8cy6dBYf/9gdmJZBLtdDIh5n3k03cejQIZKJOJs3b8Iulchm00Qsk6FDhxJ05DF0DUMX5Lq7eXXNGnK5HLFYFOl55PP9RPyMsWKxSCaTYfr06cfUhBqc/fRu0N7RzrJly6irqyUeT1BdVcX0adOZM2c24yeMo7Ozi4qKCtLJBFs2b2L48AbqamuYMH4co0eOIGJZPProT/jYHbcxrnEslmHw+sbX2bZ1K+s2N9HUtIeIaTLvpuuYdM44qiqyvgHglMsVrFu7jo6OdkrFEqahq6KppgX+VX5lRQWlQol4LIYGJOJxujs7aWtp4corPohlGJjpDB/9yK20tbXx/G9+SyIe4/UNG8jn88y6dBapdMqvPq7EyWPHjqW6urpcsfvtEDEN+np6WbFiFUIIKisqSSVTnHfeZEaNHMm8eTdRsm0K+SLxeIKuzg527dqpDG+nxEc+8mGmTZvGU796iilTJnP55ZdTWVlJR0cHLyxbRi7Xw7JlS8lms0ydMpkZF3+AeDzuGyt+oVK/pEGpVER6LnbJpq9UwLZdKrJpNKFRVVmBbZeIRiLkC0XS6RS6prF3z26iUVV2xDQjXH3VXAzDYPHixRw+dBAk7NnTxOjRo3BdhzFjRqPC2za1tdUMHzb0hDkZXGLgrRCUWQiyXFPJFGPHjuXhhx9mxowZ1NbWljMDB1U4IZ1OM6yhgUwmTcQ0qKqo4K4770Ii6erqIhqNlvenurpalVLR/xQtVwIA1+1WNdtcpTP0pFfOBFVFSD2VTckxob2QkJBTzBltSAWCccMwmD17NkuWvQBAKpXk/373n3n44Ye5995Pk8lkqR9ST2VlJVu3buHAvv185JZbiUYjxGIxFi1cSFVlparxpOskEwmi/kk4kUiQTqeprKwkGU9wy803E41YSOkRMU2y2Sz79uxl7JgxjBwxkmENDfT39bFo8WL27dvHPffcQ09Pjus/dB3/+q8PsX79evr6+mhtaeHHP/4xXV1dfPDyy7nuumsxDJ1CocCcObOJRqPMnTOH7m7VY+ziiy5i5oyLKdkOnZ2d7Nu3j1wux7hxjTQ3H6C9vZ3vf/8HdB/t4O5P3MnkyZMxDINf//rX7N+/n69+9asqhX1Qq5x3A9u22bFjB3PnzKWzq4stW7aQzWRob2/jmWeewfM8RowYwV13fZz+vn6OHj1KPBano6OD2z56Kz959DFumXcDQrrMf2I+d9/9CRrHjOGCCy6ktbWNq+aMxLzuGkxD59lnfk0yGeeDl1/KzIsvorevh717mujK9VFfP4QhQ4YgpWTBgp+zc+dOZs+ezY03XK96OKZSrFy5inx/H83NzRTyBR7/2c9YvWY1//N//o+y0BmgurqKefNuJJFIIKWkvb2D5cuXU1dTQzSqdDG9vb3cf//9/Mu//AsjRox4W3MnAKSktbWdoUPrqaurZdGixby2bi3dXV1s3LiJlpYWXMfhox/9COPHNdLU1MQ5Eyeya9cubrzhBpYsWULj2DEMa6hn/vwFmIbJNddcRX1dHRdeeCErXn6Zv/u7/0Vz8wF6enL86JEfcfGMi7niiivJZjLs3LWLzs6jdHfnWP3KamKxGCtWrKKpqYlSqcS9997LhRdOJ5XKsGrVKizLIhqN0tHeQ667m98+/zy33HqLamWjCQzdQkrJpZfOQkhVAiKfz7Nt2zZ27drN5z73OXRdAwzmz38SKT0+91efRU3FcXWb3iJB3TkltxIcPnKY+fPn8/Wvf53W1laOHj1Ka1tbWS8lkRimRUVFllyumw0bNpBOJYlYljp3bNtKe3s72UwWXVMJKfPnz6dYKPL5z3/+be0jgKYlAB3Py7Nly3x0fRsbN80nkbyYfGEXAF1d+xgxsocjR9qoqR6omB8SEnLqOS0rm/8xBILmQEOxfv16VqxcxSc//ZlyFfPly5fzne/8M7fcfDP3fPIe1WFe1+gv9JNIJHAch71Ne/jrL9zPt7/9bSZOmOhXbPZwBDiuw4b1G8ik00ycMAGnZBOJWOUSB2gaff39/PSn/8nuXbtV6rnrYpdKJJJJbr/9diZPnoSUklwux4MPPshdd32CbDarepn5NZVi0Ri6Yfq6B+U9CwpdHjx4kK1btzN79pUqm7BU5Pe/X0R/fz+NjY3U1tYSi0UQQqNYKGDognQqVa5zdPToUQqFAjU1NViWVZ6vd84r5fr/K8PT86DkOEjgwQe/zcxLZjF61GjisaiqEI8gEomg+/WpHNtWdYYsi57eXp566mk+ec+fqyzKYqkcClm7dh2pVJJxE87h0UcfY86cK8lksjz55BMk4jFuvvkmduzYzo7t2xnbOIFhw0cQiUTK9aFsu0Q0GqOqSmmlcrkcpaKNFTHRNQPLMmjv6GD9+nVcc801fjudgezEoMaYJmDb9h185zv/zH1/cS+TJk8maho4jk1raysVFRWkUqljanX90TMpJbbrYdsOpmmwceNGli57gVtuuYVkIqHCrZqOoWtEo1FAUCwWiESi9Pf3EY3GePLJBcydO5va2rqyJ8d1Xdrb2jhw8CCTJp3LwYOH2bFjB9deexWrVq1i1apV3HDD9VRWVrJo0WIiEYvGxnHU1NRgGDqGrsJJtuNSkc0Qi8cpFoq0d7STiCcQmoauK3fO4sVLuPLKK0il0v4c4O+Dh/T8el+ux4PffpBstoL7/uIvysdjW3s7AhhSVwcca0i91bmUUnUocD3Vl7Gvr49NmzYxcsRI6urq8KRHS0sLTz/1FAsXLiQej3PVVVdz64dvJZPOUiwW0AyDYrGkGiF7Ht/81jeRUvLlL39ZlRARGp2dnXjSI51KE41G/YKpb20/pZSsWFGN43Se8Pz69fCFL8DcufC3fzuwfdjwLzN2zANo2rGVzYUQ/wD8PWEdqZCQ94wz1pCCgZNQgD0oXTp4rrn5AJomGD58uL8Y+i1EUFW5PcelrbWVutpav2KypxZOv1yC47joQrWLCVCeA7/Ao/9ZhUKR3t4eNE15O5SeZGA/Pc+jvaOd2praY/YvMJ6kHOgVNvh1juNSLBZIJpPHjDtYWAbuqwXTGFS9eXD7jONf985pLI41pKQcUEsdaWmhsqoKU9dVxery2AY+O2g9AmDbDv35fjLpjD8nJ+67J+HIkRaymQyRSMQXzstj5kzzNXHBQh5sDxh4T3ULdLvB/qnPCl4z0C5l8Pt0dnZRWVmhwniD2pcc/xlvBU9KNZuq7Df5fJ5isUQ2my0fG1o5k+3kIa+enh5isVg5tFiuOO+3QJES+vvz9PX1UV1dNVCfTQz+TgZVqvcNyMHzU55nbaBHoX8Ilud/sCcoaD4sCArUehQKBYBjuxOgKp2XW+EM4u0YUnLQ/cG/hcFjclyXI0cO093dTXV1DTXV1SdU6w/a7xw9ehTTMkkmkwwu8gl+uyFxsspRf9y+bt78YUqlIwPb/AuqV9d0cd99W7nuumq+/o1x5efr6u6iYeh9oSEVEnIacEaH9o6/UrUs65jnAEaNGlneFiwuepAJA2iGofRHUlV/DtKuNV9xrVtGkAOk3sTXkyL8Nhz+PsTjMeLx2En2UvqeBI0hdbXHbCvndzNgYAxeL4QQWJaGZZknjPtk94Mq5GLQ9j91cX+rCBEo0lChNfCrKMHJPr7cmw2JZelYVorAKyeOnQykVAnt9bV1KtFNMNC3D1SvM/++rh+bR3HC2AOjINgsgpcOGGUDrzlxDquqKv3P8atavwNzK4SSnAcHWDKRIJGID+xB+Tv9L8aE0vecDF0bqBcZj0eJx6LlRELFYENz4PHAPJ54bJb3WR/0el9opP4dKJyp+30FhZDouiCRCH4rctD7voPH6iCj5vjfwmD9mqbr1Dc0UN/QoBo2o37r6k+D8JkSt1dVVZ3kY96Z733y5F+VH8tBNdG7cy8As6movIZp03/6J39WSEjIO89p4ZESQrQBfUD7qd6X94Bqzv5xvh/GCOE4zzZOx3GmgPHAUWDPSZ4fKaWsCR6EHqmQkPee08IjJaWsEUK8+n7oYv5+GOf7YYwQjvNs43QZpxDiZuBm/+EQlCHVBWzyt7VLKb806O+/DPh9qzjf//8eIcSl/v3loVEVEvLucVoYUiEhISEhZc4H7j5u2xj/BrAP+NKg564FPnjc31/i3wJCQyok5F0iNKRCQkJCTiOklP8A/MNb+Psr3q19CQkJeXNOp8rmj5zqHXiPeD+M8/0wRgjHebbxfhlnSEjIO8hpITYPCQkJCQkJCTkTOZ08UiEhISEhISEhZxSn3JASQlwrhNguhNjlZ5+csQghfiyEaBVCbBq0rVIIsUgIsdP/v2LQc1/xx71dCHHNqdnrt44QYrgQYpkQYqsQYrMQ4vP+9rNqrEKIqBBitRBigz/Of/S3n1XjBBBC6EKI14QQz/mPz8Yx7hVCbBRCrBdCvOpvO+vGGRIS8t5ySg0pIYQO/BC4DjgXuEMIce6p3Kc/kZ+gMmgG82VgiZRyHLDEf4w/ztuBSf5r/tWfjzMBB/iilPIcYAbwWX88Z9tYi8BsKeVUVCbVtUKIGZx94wT4PLB10OOzcYwAV0opzx9U5uBsHWdISMh7xKn2SF0E7JJSNkkpS8B8YN4p3qe3jZTyD6jCeYOZBzzm33+Mgfow84D5UsqilHIPsAs1H6c9UsrDUsp1/v0e1ALcwFk2Vqno9R+a/k1ylo1TCDEMuJ5jU+TPqjG+Ae+XcYaEhLxLnGpDqgFoHvT4gL/tbKJOSnkYlAECBH1izoqxCyFGAdOAVzgLx+qHvNYDrcAiKeXZOM7vAv+DgZ4ocPaNEZQRvFAIsVYIca+/7WwcZ0hIyHvIqa4jdbJGVe+XNMIzfuxCiCTwS+B+KWXuDfqOnbFjlVK6wPlCiCzwlBBi8hv8+Rk3TiHEDUCrlHKtEOKKP+YlJ9l2Wo9xELOklIeEELXAIiHEtjf42zN5nCEhIe8hp9ojdQAYPujxMODQKdqXd4sWIUQ9gP9/q7/9jB67EMJEGVGPSymDjqtn5VgBpJRdwAsovczZNM5ZwE1CiL2o0PpsIcR/cnaNEQAp5SH//1bgKVSo7qwbZ0hIyHvLqTak1gDjhBCjhRAWStz57Cnep3eaZxlo93A38Myg7bcLISJCiNHAOGD1Kdi/t4xQrqd/B7ZKKf950FNn1ViFEDW+JwohRAyYC2zjLBqnlPIrUsphUspRqN/fUinlnZxFYwQQQiSEEKngPnA1qnfdWTXOkJCQ955TGtqTUjpCiL8Cfg/owI+llJtP5T79KQghngCuAKqFEAdQXdj/D7BACPEpYD/wUQAp5WYhxAJgCyoL7rN+GOlMYBZwF7DR1w8BfJWzb6z1wGN+tpYGLJBSPieEWMnZNc6TcbZ9l3Wo0Cyo897PpJS/E0Ks4ewaZ0hIyHtMWNk8JCQkJCQkJORtcqpDeyEhISEhISEhZyyhIRUSEhISEhIS8jYJDamQkJCQkJCQkLdJaEiFhISEhISEhLxNQkMqJCQkJCQkJORtEhpSISEhISEhISFvk9CQCgkJCQkJCQl5m4SGVEhISEhISEjI2+T/AzGhkWj+r+sTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4.提取手写文本\n",
    "\n",
    "import json\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "import time\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "text_recognition_url = endpoint + \"/vision/v3.0/read/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to recognize.\n",
    "image_url = \"https://img.juzibashi.com/uploadfile/zspics/2019092905132979.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"15f98ea70f74425382a7f6b768cd9915\"}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(text_recognition_url, headers=headers, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# Extracting text requires two API calls: One call to submit the\n",
    "# image for processing, the other to retrieve the text found in the image.\n",
    "\n",
    "# Holds the URI used to retrieve the recognized text.\n",
    "operation_url = response.headers[\"Operation-Location\"]\n",
    "\n",
    "# The recognized text isn't immediately available, so poll to wait for completion.\n",
    "analysis = {}\n",
    "poll = True\n",
    "while (poll):\n",
    "    response_final = requests.get(\n",
    "        response.headers[\"Operation-Location\"], headers=headers)\n",
    "    analysis = response_final.json()\n",
    "    \n",
    "    print(json.dumps(analysis, indent=4))\n",
    "\n",
    "    time.sleep(1)\n",
    "    if (\"analyzeResult\" in analysis):\n",
    "        poll = False\n",
    "    if (\"status\" in analysis and analysis['status'] == 'failed'):\n",
    "        poll = False\n",
    "\n",
    "polygons = []\n",
    "if (\"analyzeResult\" in analysis):\n",
    "    # Extract the recognized text, with bounding boxes.\n",
    "    polygons = [(line[\"boundingBox\"], line[\"text\"])\n",
    "                for line in analysis[\"analyzeResult\"][\"readResults\"][0][\"lines\"]]\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image)\n",
    "for polygon in polygons:\n",
    "    vertices = [(polygon[0][i], polygon[0][i+1])\n",
    "                for i in range(0, len(polygon[0]), 2)]\n",
    "    text = polygon[1]\n",
    "    patch = Polygon(vertices, closed=True, fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(vertices[0][0], vertices[0][1], text, fontsize=20, va=\"top\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学习人脸识别和计算机视觉心得体会\n",
    "\n",
    "> 历程艰辛！    \n",
    "> 不会放弃！    \n",
    "> 终得成果！    \n",
    "> 分享经验吧～"
   ]
  }
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